GEOeinheiten Parameterauswahl für 100pergeomorph
fabian gruber
April 24, 2017
ALL TERRAIN
SVM n=100
## [1] "Prediction error at end is: 0.787864153294906"
## [2] "Prediction error at end is: 0.673634262865452"
## [3] "Prediction error at end is: 0.581855948486265"
## [4] "Prediction error at end is: 0.506383393051877"
## [5] "Prediction error at end is: 0.467109362829101"
## [6] "Prediction error at end is: 0.441621228644129"
## [7] "Prediction error at end is: 0.427339409046061"
## [8] "Prediction error at end is: 0.424797476242405"
## [9] "Prediction error at end is: 0.411534766578387"
## [10] "Prediction error at end is: 0.403897284104482"
## k 1 k 2
## 1 ChannelNetworkBaseLevel Channel_Network_Base_Level
## 2 Texture slope_DTM_50m_avg_ws7
## 3 slope_DTM_50m_avg_ws7 Texture
## 4 Modified_Catchment_Area ChannelNetworkBaseLevel
## 5 Channel_Network_Base_Level Closed_Depressions
## 6 profc_DTM_50m_avg_ws7 TPI_i0m_o500m
## 7 Catchment_slope Topographic_Wetness_Index
## 8 slope_ws7 VerticalDistancetoChannelNetwork
## 9 Convexity Convexity
## 10 VerticalDistancetoChannelNetwork Catchment_slope
## k 3 k 4
## 1 Channel_Network_Base_Level slope_DTM_50m_avg_ws3
## 2 slope_DTM_50m_avg_ws7 Channel_Network_Base_Level
## 3 Texture Texture
## 4 ChannelNetworkBaseLevel ChannelNetworkBaseLevel
## 5 Modified_Catchment_Area TPI_i0m_o500m
## 6 VerticalDistancetoChannelNetwork saga_Topographic_Wetness_Index_hr
## 7 Catchment_slope Catchment_slope
## 8 Topographic_Wetness_Index longc_DTM_50m_avg_ws7
## 9 TPI_i0m_o400m Modified_Catchment_Area
## 10 Convexity slope_ws11
## k 5
## 1 ChannelNetworkBaseLevel
## 2 Catchment_slope
## 3 slope_DTM_50m_avg_ws7
## 4 Channel_Network_Base_Level
## 5 Texture
## 6 Closed_Depressions
## 7 TPI_i0m_o500m
## 8 Topographic_Wetness_Index
## 9 sagaTopographic_Wetness_Index
## 10 LS_Factor
## allchosen Freq
## 1 Catchment_slope 5
## 2 ChannelNetworkBaseLevel 5
## 3 Channel_Network_Base_Level 5
## 16 Texture 5
## 13 slope_DTM_50m_avg_ws7 4
## 5 Convexity 3
## 8 Modified_Catchment_Area 3
## 17 Topographic_Wetness_Index 3
## 19 TPI_i0m_o500m 3
## 20 VerticalDistancetoChannelNetwork 3
## 4 Closed_Depressions 2## [1] "10fold cv-error: 0.378117048346056 for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Modified_Catchment_Area AND VerticalDistancetoChannelNetwork AND Catchment_slope AND Topographic_Wetness_Index AND TPI_i0m_o400m AND Convexity"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 33 16 0 3 0 0 0 0 0 0 0 10 2 0 0 0 0
## Ant 7 52 0 2 0 1 1 1 0 0 0 5 1 3 2 0 0
## CBD 0 0 81 0 3 0 0 0 2 0 0 0 2 2 15 0 1
## CD 6 4 0 41 0 12 4 0 0 2 3 0 5 1 1 0 9
## CSR 0 0 0 0 84 0 0 0 0 0 0 0 0 0 6 0 0
## DC 9 0 0 7 0 68 2 0 0 0 0 1 1 0 0 0 3
## GLD 12 14 0 17 0 1 167 23 0 0 12 0 11 3 3 0 3
## IMS 5 0 1 7 0 3 13 68 0 0 2 0 5 3 1 0 1
## ISR 0 0 2 0 2 0 0 0 83 1 0 0 11 0 11 4 10
## LD 2 1 2 3 0 1 7 0 0 67 3 0 1 5 7 0 8
## LT 4 0 0 2 0 1 4 0 0 6 67 0 0 0 1 0 17
## MrD 10 4 0 0 0 3 0 0 0 0 0 66 0 0 0 0 0
## MxD 1 7 9 8 0 7 0 2 3 8 2 0 55 3 8 2 7
## SB 1 0 0 1 0 0 0 0 0 1 2 1 0 61 8 0 5
## SD 1 1 4 0 2 0 1 2 2 6 1 0 0 6 29 0 0
## SSR 0 0 0 3 0 0 0 0 6 0 0 0 2 4 4 89 8
## TG 0 0 2 7 0 3 0 1 4 9 8 0 4 9 4 5 129
## WB 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 0
## CSR 0
## DC 0
## GLD 4
## IMS 1
## ISR 0
## LD 0
## LT 0
## MrD 0
## MxD 2
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 93
## [1] "Kappa overall = 0.657125344943974"
## [1] "Tau overall = 0.659452177817692"
## [1] "mean quality = 0.526415826968994"
## [1] "The quality is 0.526415826968994"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.686505135607319"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 59 29 0 1 0 1 0 0 0 0 0 16 2 0 1 0
## Ant 15 83 0 4 0 0 3 1 0 2 0 10 1 2 4 0
## CBD 0 0 146 0 13 0 0 0 7 1 0 0 13 1 32 0
## CD 14 14 0 67 0 21 10 0 0 5 12 0 19 1 2 0
## CSR 0 0 0 0 142 0 0 0 0 0 0 0 0 0 10 0
## DC 20 0 0 9 0 120 10 1 0 3 1 4 0 0 0 0
## GLD 20 43 0 48 0 18 304 58 0 0 26 3 19 7 2 0
## IMS 10 0 3 13 0 4 35 116 0 0 4 0 5 6 6 0
## ISR 0 0 11 2 8 0 0 0 130 0 0 0 28 2 30 20
## LD 8 4 16 6 0 3 21 0 0 128 10 0 0 13 11 0
## LT 6 6 0 5 0 0 8 0 0 12 120 0 6 7 2 0
## MrD 28 2 0 1 0 5 0 0 0 0 0 143 1 0 0 0
## MxD 1 11 5 24 0 21 0 10 20 12 8 0 83 2 8 21
## SB 1 0 0 0 2 0 0 1 1 3 7 3 0 122 12 0
## SD 0 5 11 0 6 1 1 7 2 13 0 0 7 9 61 0
## SSR 0 0 0 3 3 0 0 0 29 0 0 0 7 6 3 150
## TG 4 0 7 19 1 7 0 3 10 21 12 0 7 19 9 8
## WB 0 0 0 0 0 0 9 0 0 0 0 3 0 1 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 0 0
## CBD 6 0
## CD 16 4
## CSR 0 0
## DC 10 0
## GLD 13 9
## IMS 6 0
## ISR 33 0
## LD 20 6
## LT 27 1
## MrD 0 3
## MxD 15 2
## SB 11 0
## SD 0 1
## SSR 10 0
## TG 232 0
## WB 0 175
## [1] "classification error rate with altdata: 0.393839103869654"
## [1] "Kappa overall = 0.580010208394313"
## [1] "Tau overall = 0.582993890020367"
## [1] "mean quality = 0.450640220514493"
## [1] "The quality is 0.450640220514493"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.622655565891132"SVM n=200
## [1] "Prediction error at end is: 0.739019209216254"
## [2] "Prediction error at end is: 0.64581769489405"
## [3] "Prediction error at end is: 0.555157426955456"
## [4] "Prediction error at end is: 0.501533544452264"
## [5] "Prediction error at end is: 0.451994552610316"
## [6] "Prediction error at end is: 0.423391195558683"
## [7] "Prediction error at end is: 0.397850692000938"
## [8] "Prediction error at end is: 0.374103396147731"
## [9] "Prediction error at end is: 0.365163421690515"
## [10] "Prediction error at end is: 0.358779354653739"
## k 1 k 2
## 1 ChannelNetworkBaseLevel ChannelNetworkBaseLevel
## 2 Texture Texture
## 3 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7
## 4 Channel_Network_Base_Level TPI_i0m_o400m
## 5 TPI_i0m_o500m Closed_Depressions
## 6 Closed_Depressions Convexity
## 7 Convexity Channel_Network_Base_Level
## 8 VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 9 Topographic_Wetness_Index Catchment_slope
## 10 Catchment_slope slope_ws19_hr
## k 3 k 4
## 1 ChannelNetworkBaseLevel ChannelNetworkBaseLevel
## 2 Catchment_slope Texture
## 3 slope_DTM_50m_avg_ws5 slope_DTM_50m_avg_ws7
## 4 Texture Closed_Depressions
## 5 Closed_Depressions TPI_i0m_o500m
## 6 Channel_Network_Base_Level Channel_Network_Base_Level
## 7 VerticalDistancetoChannelNetwork Catchment_slope
## 8 Convexity Convexity
## 9 TPI_i0m_o500m VerticalDistancetoChannelNetwork
## 10 slope_ws5 DiurnalAnisotropicHeating
## k 5
## 1 ChannelNetworkBaseLevel
## 2 Texture
## 3 slope_DTM_50m_avg_ws7
## 4 Channel_Network_Base_Level
## 5 TPI_i0m_o500m
## 6 Closed_Depressions
## 7 Catchment_slope
## 8 Convexity
## 9 VerticalDistancetoChannelNetwork
## 10 slope_ws29_hr
## allchosen Freq
## 1 Catchment_slope 5
## 2 ChannelNetworkBaseLevel 5
## 3 Channel_Network_Base_Level 5
## 4 Closed_Depressions 5
## 5 Convexity 5
## 12 Texture 5
## 16 VerticalDistancetoChannelNetwork 5
## 8 slope_DTM_50m_avg_ws7 4
## 15 TPI_i0m_o500m 4## [1] "10fold cv-error: 0.358452138492872 for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Modified_Catchment_Area AND VerticalDistancetoChannelNetwork AND Catchment_slope AND Topographic_Wetness_Index AND TPI_i0m_o400m AND Convexity"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 96 31 0 1 0 1 0 0 0 0 1 14 0 0 1 0 0
## Ant 2 84 0 1 0 4 4 0 0 6 3 3 3 4 6 0 0
## CBD 0 0 161 0 8 1 0 0 6 1 0 0 12 3 26 0 5
## CD 18 15 0 87 0 11 9 3 0 3 3 2 27 2 2 0 9
## CSR 0 0 1 0 146 0 0 0 0 0 0 0 0 0 10 0 0
## DC 11 3 1 15 0 144 7 1 0 7 3 3 0 0 0 0 15
## GLD 15 38 0 51 0 15 352 39 0 1 22 1 16 6 2 0 12
## IMS 8 1 2 10 0 4 10 142 0 0 3 0 7 2 5 0 2
## ISR 0 0 8 2 8 0 0 0 150 0 1 0 19 3 21 7 36
## LD 2 4 7 3 0 2 4 0 0 148 11 0 1 15 17 0 21
## LT 8 7 0 6 0 0 2 0 0 4 133 0 2 11 4 0 29
## MrD 23 8 0 0 0 4 0 0 0 0 0 157 2 0 0 0 0
## MxD 1 5 5 5 0 9 0 0 13 5 5 0 85 1 7 4 7
## SB 1 0 1 0 0 1 0 2 0 4 4 0 0 127 10 2 5
## SD 0 0 7 0 10 0 0 0 6 0 0 0 7 2 68 0 1
## SSR 0 0 0 2 3 0 0 0 14 0 0 0 7 3 5 177 12
## TG 1 1 6 19 0 5 2 10 10 21 11 0 10 18 9 9 246
## WB 0 0 0 0 0 0 11 0 0 0 0 2 0 1 0 0 0
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 0
## CSR 0
## DC 2
## GLD 4
## IMS 0
## ISR 0
## LD 2
## LT 2
## MrD 2
## MxD 1
## SB 0
## SD 1
## SSR 0
## TG 1
## WB 186
## [1] "Kappa overall = 0.663525690742074"
## [1] "Tau overall = 0.666017730921289"
## [1] "mean quality = 0.530333884016433"
## [1] "The quality is 0.530333884016433"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.689100945495232"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 39 23 0 0 0 6 0 0 0 0 0 14 0 1 0 0
## Ant 8 43 0 5 0 0 2 1 0 6 0 1 1 3 3 0
## CBD 0 0 78 0 2 0 0 0 3 5 0 0 2 1 13 0
## CD 6 5 0 40 0 7 9 1 0 1 2 0 11 3 0 0
## CSR 0 0 1 0 78 0 0 0 0 0 0 0 0 0 4 0
## DC 5 1 0 10 0 73 4 0 0 4 0 1 3 0 0 0
## GLD 10 13 0 19 0 2 167 24 0 1 12 0 8 1 0 0
## IMS 4 0 3 7 0 2 7 61 0 0 2 0 9 2 3 0
## ISR 0 0 4 1 1 1 0 0 80 1 0 0 13 1 14 9
## LD 3 1 3 3 0 1 1 0 0 67 5 0 1 7 13 0
## LT 3 0 0 3 0 1 4 0 0 2 65 0 0 3 2 0
## MrD 11 8 0 0 0 5 0 0 0 0 0 66 0 0 0 0
## MxD 0 5 5 2 0 1 0 1 5 3 0 0 37 2 6 1
## SB 1 0 0 1 0 0 0 1 0 2 6 0 0 58 9 0
## SD 0 0 5 0 10 0 1 0 3 0 1 0 3 2 27 0
## SSR 0 0 0 1 0 0 0 0 4 0 0 0 2 4 3 81
## TG 1 0 2 9 0 1 1 8 5 8 7 0 10 12 3 9
## WB 0 0 0 0 0 0 4 0 0 0 0 2 0 0 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 0 0
## CBD 2 0
## CD 6 1
## CSR 0 0
## DC 10 0
## GLD 3 3
## IMS 0 1
## ISR 11 0
## LD 11 0
## LT 13 1
## MrD 0 1
## MxD 5 1
## SB 7 0
## SD 0 0
## SSR 9 0
## TG 123 0
## WB 0 92
## [1] "classification error rate with altdata: 0.351145038167939"
## [1] "Kappa overall = 0.625607109612883"
## [1] "Tau overall = 0.628199371351594"
## [1] "mean quality = 0.489488966354227"
## [1] "The quality is 0.489488966354227"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.656597715709897"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=1,altdata=twohundred,legend=geolegendeng)## Loading required package: randomForest## randomForest 4.6-10## Type rfNews() to see new features/changes/bug fixes.## [1] "OBB error with all predictors of allterraincols is 0.368389780154486"
## MeanDecreaseGini parameters
## ChannelNetworkBaseLevel 59.40663 ChannelNetworkBaseLevel
## Channel_Network_Base_Level 49.80239 Channel_Network_Base_Level
## Catchment_slope 30.71719 Catchment_slope
## Texture 30.64480 Texture
## slope_DTM_50m_avg_ws7 27.62740 slope_DTM_50m_avg_ws7
## Modified_Catchment_Area 27.47028 Modified_Catchment_Area
## Protection_Index 21.42085 Protection_Index
## slope_DTM_50m_avg_ws5 19.62595 slope_DTM_50m_avg_ws5
## Closed_Depressions 19.51936 Closed_Depressions
## TPI_i0m_o500m 19.10673 TPI_i0m_o500m
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 50 12 0 1 0 1 2 0 0 0 1 3 0 0 0 0
## Ant 14 109 0 1 0 2 6 2 0 0 0 7 2 1 4 0
## CBD 0 0 45 0 2 0 0 3 7 4 0 0 7 0 11 3
## CD 16 9 0 82 0 22 11 3 0 7 13 1 13 0 3 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 22 1 0 16 0 128 7 0 0 1 1 5 3 0 0 0
## GLD 23 23 0 37 0 10 323 38 0 2 41 2 20 9 6 1
## IMS 7 5 0 8 0 1 9 120 0 0 3 0 0 1 3 0
## ISR 0 0 26 1 6 0 0 0 108 0 3 0 17 4 28 18
## LD 4 4 13 5 0 4 1 0 0 146 8 0 4 9 7 0
## LT 4 4 0 3 0 1 7 0 0 1 96 0 1 15 1 1
## MrD 29 6 0 0 0 1 3 0 0 0 0 154 0 0 0 0
## MxD 0 4 7 9 0 11 0 3 9 4 7 0 80 4 6 14
## SB 4 6 7 0 7 0 4 5 3 2 6 1 0 115 9 0
## SD 1 0 14 1 4 0 2 1 11 13 0 0 2 10 31 0
## SSR 0 0 0 6 3 1 0 0 24 2 0 0 5 8 4 133
## TG 0 1 10 17 0 5 1 8 8 5 8 0 9 12 11 13
## WB 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 0 1
## CBD 3 0
## CD 7 4
## CSR 0 0
## DC 11 0
## GLD 14 6
## IMS 2 1
## ISR 30 0
## LD 16 0
## LT 30 1
## MrD 0 3
## MxD 13 0
## SB 7 0
## SD 1 0
## SSR 10 0
## TG 228 0
## WB 0 173
## [1] "classification error rate with altdata: 0.371555555555556"
## [1] "Prediction error at end is: 0.669565612504544"
## [2] "Prediction error at end is: 0.522186737290336"
## [3] "Prediction error at end is: 0.407441449862388"
## [4] "Prediction error at end is: 0.343193384223919"
## [5] "Prediction error at end is: 0.292197642415745"
## [6] "Prediction error at end is: 0.277930103339046"
## [7] "Prediction error at end is: 0.2636586695747"
## [8] "Prediction error at end is: 0.265186685361167"
## [9] "Prediction error at end is: 0.265691696525939"
## [10] "Prediction error at end is: 0.266708210001558"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 slope_DTM_50m_avg_ws7 ChannelNetworkBaseLevel
## 3 ChannelNetworkBaseLevel slope_DTM_50m_avg_ws7
## 4 Texture Texture
## 5 VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 6 Catchment_Area2 Catchment_slope
## 7 Convexity TPI_i0m_o300m
## 8 maxic_ws5_hr profc_ws11_hr
## 9 profc_DTM_50m_avg_ws7 Catchment_Area2
## 10 slope_ws13_hr TPI_i0m_o500m
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 slope_DTM_50m_avg_ws7 ChannelNetworkBaseLevel
## 3 ChannelNetworkBaseLevel Texture
## 4 TPI_i0m_o500m VerticalDistancetoChannelNetwork
## 5 Texture slope_DTM_50m_avg_ws7
## 6 Catchment_slope TPI_i0m_o500m
## 7 Convexity Catchment_slope
## 8 longc_ws11 Convexity
## 9 slope_ws11_hr longc_ws23_hr
## 10 TPI_i0m_o10m maxic_ws19_hr
## k 5
## 1 Channel_Network_Base_Level
## 2 ChannelNetworkBaseLevel
## 3 Catchment_slope
## 4 VerticalDistancetoChannelNetwork
## 5 Texture
## 6 TPI_i0m_o140m
## 7 TPI_i0m_o500m
## 8 slope_ws15
## 9 profc_DTM_50m_avg_ws5
## 10 CrossSectionalCurvature
## allchosen Freq
## 3 ChannelNetworkBaseLevel 5
## 4 Channel_Network_Base_Level 5
## 18 Texture 5
## 2 Catchment_slope 4
## 14 slope_DTM_50m_avg_ws7 4
## 22 TPI_i0m_o500m 4
## 23 VerticalDistancetoChannelNetwork 4
## 5 Convexity 3
## 1 Catchment_Area2 2predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","slope_DTM_50m_avg_ws7","Texture","ChannelNetworkBaseLevel", "Catchment_Area2","VerticalDistancetoChannelNetwork","Convexity"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.251399491094148 for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Catchment_Area2 AND VerticalDistancetoChannelNetwork AND Convexity"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 89 8 0 3 0 6 2 1 0 0 4 6 4 0 1 0
## Ant 11 144 0 4 0 5 4 4 0 0 2 9 0 7 3 0
## CBD 0 0 158 1 9 0 0 0 6 2 1 0 12 2 25 0
## CD 12 14 0 100 0 11 3 0 1 2 9 0 12 4 1 0
## CSR 0 0 5 0 143 0 0 0 1 0 0 0 0 0 19 1
## DC 16 0 0 10 0 128 9 1 0 2 2 2 1 0 0 0
## GLD 13 11 0 30 0 19 358 7 0 0 9 1 7 4 1 0
## IMS 4 4 1 10 0 2 14 182 0 0 2 0 0 4 0 0
## ISR 0 0 1 1 12 0 0 0 146 0 2 0 15 3 20 17
## LD 1 2 7 7 0 7 0 0 0 164 2 0 0 5 5 0
## LT 6 2 0 11 0 0 4 0 0 0 128 0 3 6 0 1
## MrD 23 0 0 0 0 2 0 0 0 0 0 153 0 0 0 0
## MxD 2 1 9 10 0 15 0 1 20 3 10 0 118 6 8 10
## SB 1 4 8 2 1 2 1 1 2 4 8 2 0 129 25 0
## SD 1 6 6 2 9 0 0 0 0 11 0 0 11 9 77 0
## SSR 0 0 0 0 0 1 0 0 20 1 0 0 10 4 1 169
## TG 7 1 4 9 1 2 0 0 3 11 21 0 5 13 6 1
## WB 0 0 0 2 0 1 6 0 0 0 0 9 0 2 1 0
##
## altpreds TG WB
## AD 0 1
## Ant 0 0
## CBD 4 0
## CD 13 1
## CSR 0 0
## DC 12 1
## GLD 6 3
## IMS 5 0
## ISR 22 0
## LD 22 4
## LT 11 4
## MrD 0 1
## MxD 21 0
## SB 13 0
## SD 6 0
## SSR 9 0
## TG 256 0
## WB 0 186
## [1] "classification error rate with altdata: 0.280040733197556"
## [1] "Kappa overall = 0.701880811385382"
## [1] "Tau overall = 0.703486282496705"
## [1] "mean quality = 0.565979175376853"
## [1] "The quality is 0.565979175376853"
## [1] "######### Cramer's V = 0.715324152797654"RandomForest n=200
## [1] "Prediction error at end is: 0.617982250371413"
## [2] "Prediction error at end is: 0.482891873224386"
## [3] "Prediction error at end is: 0.361083418041546"
## [4] "Prediction error at end is: 0.28472678343368"
## [5] "Prediction error at end is: 0.255105298824511"
## [6] "Prediction error at end is: 0.240549821461152"
## [7] "Prediction error at end is: 0.230081189563948"
## [8] "Prediction error at end is: 0.222933119607996"
## [9] "Prediction error at end is: 0.219612231865926"
## [10] "Prediction error at end is: 0.219104308390023"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 ChannelNetworkBaseLevel ChannelNetworkBaseLevel
## 3 Texture Catchment_slope
## 4 VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 5 Catchment_slope Texture
## 6 Convexity TPI_i0m_o500m
## 7 TPI_i0m_o400m slope_ws15
## 8 slope_ws15 Convexity
## 9 Longitudinal_Curvature Catchment_Area2
## 10 Modified_Catchment_Area Modified_Catchment_Area
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 ChannelNetworkBaseLevel ChannelNetworkBaseLevel
## 3 slope_DTM_50m_avg_ws7 Texture
## 4 Texture VerticalDistancetoChannelNetwork
## 5 TPI_i0m_o400m Catchment_slope
## 6 Convexity slope_ws15
## 7 slope_ws11 Convexity
## 8 Catchment_slope TPI_i0m_o500m
## 9 Modified_Catchment_Area Modified_Catchment_Area
## 10 Longitudinal_Curvature longc_ws19_hr
## k 5
## 1 Channel_Network_Base_Level
## 2 slope_DTM_50m_avg_ws5
## 3 ChannelNetworkBaseLevel
## 4 Texture
## 5 TPI_i0m_o500m
## 6 Convexity
## 7 RelativeSlopePosition
## 8 Catchment_slope
## 9 Catchment_slope_hr
## 10 VerticalDistancetoChannelNetwork
## allchosen Freq
## 2 Catchment_slope 5
## 4 ChannelNetworkBaseLevel 5
## 5 Channel_Network_Base_Level 5
## 6 Convexity 5
## 15 Texture 5
## 9 Modified_Catchment_Area 4
## 18 VerticalDistancetoChannelNetwork 4
## 14 slope_ws15 3
## 17 TPI_i0m_o500m 3
## 8 Longitudinal_Curvature 2
## 16 TPI_i0m_o400m 2predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","slope_DTM_50m_avg_ws7","Texture","ChannelNetworkBaseLevel", "Catchment_Area2","VerticalDistancetoChannelNetwork","Convexity"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.213849287169043 for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Catchment_Area2 AND VerticalDistancetoChannelNetwork AND Convexity"
## [1] "Kappa overall = 0.999458070778881"
## [1] "Tau overall = 0.999460884149994"
## [1] "mean quality = 0.99916455354319"
## [1] "The quality is 0.99916455354319"
## [1] "######### Cramer's V = 0.999558344451984"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 48 5 0 1 0 8 2 0 0 0 0 5 1 0 0 0
## Ant 9 84 0 6 0 1 2 2 0 0 1 0 0 2 1 0
## CBD 0 0 84 0 4 0 0 0 2 0 0 0 6 3 9 0
## CD 5 1 0 54 0 5 2 1 1 1 2 0 2 0 2 0
## CSR 0 0 6 0 81 0 0 0 1 0 0 0 0 0 8 0
## DC 4 1 0 9 0 75 3 0 0 2 0 1 1 0 0 0
## GLD 6 2 0 11 0 4 184 6 0 0 8 0 4 1 0 0
## IMS 1 0 0 4 0 0 2 88 0 0 2 0 1 0 1 0
## ISR 0 0 0 0 1 1 0 0 79 1 0 0 7 2 5 4
## LD 1 0 0 1 0 1 0 0 0 90 2 0 1 1 8 0
## LT 4 0 1 5 0 0 2 0 0 0 71 0 3 5 1 1
## MrD 7 6 0 0 0 2 2 0 0 0 0 77 0 0 0 0
## MxD 3 0 3 5 0 2 0 0 5 0 2 0 71 5 4 1
## SB 1 0 1 1 0 0 0 0 1 2 4 0 0 61 13 1
## SD 0 0 2 0 5 0 0 0 6 1 2 0 1 9 40 0
## SSR 0 0 0 1 0 0 0 0 2 0 1 0 0 3 3 93
## TG 2 0 4 3 0 1 1 0 3 3 5 0 2 8 5 0
## WB 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
##
## altpreds TG WB
## AD 2 0
## Ant 0 0
## CBD 1 0
## CD 3 0
## CSR 0 0
## DC 6 0
## GLD 1 2
## IMS 1 0
## ISR 10 0
## LD 4 0
## LT 7 0
## MrD 0 0
## MxD 5 0
## SB 10 1
## SD 3 0
## SSR 6 0
## TG 142 1
## WB 0 96
## [1] "classification error rate with altdata: 0.227480916030534"
## [1] "Kappa overall = 0.757820838345675"
## [1] "Tau overall = 0.759137853614728"
## [1] "mean quality = 0.634164152539504"
## [1] "The quality is 0.634164152539504"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.768004942541285"LOCAL TERRAIN
SVM n=100
## [1] "Prediction error at end is: 0.796222152983331"
## [2] "Prediction error at end is: 0.736098561562029"
## [3] "Prediction error at end is: 0.691769226774679"
## [4] "Prediction error at end is: 0.661217998649842"
## [5] "Prediction error at end is: 0.646958248948434"
## [6] "Prediction error at end is: 0.64492132730955"
## [7] "Prediction error at end is: 0.647467154800852"
## [8] "Prediction error at end is: 0.646461027158955"
## [9] "Prediction error at end is: 0.645952121306538"
## [10] "Prediction error at end is: 0.641889962091707"
## k 1 k 2 k 3
## 1 slope_DTM_50m_avg_ws7 Slope slope_DTM_50m_avg_ws5
## 2 Convexity longc_DTM_50m_avg_ws7 Convexity
## 3 profc_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7 profc_DTM_50m_avg_ws5
## 4 crosc_DTM_50m_avg_ws7 Convexity Longitudinal_Curvature
## 5 slope_ws29_hr planc_DTM_50m_avg_ws7 slope_ws7
## 6 maxic_DTM_50m_avg_ws5 maxic_DTM_50m_avg_ws5 planc_ws7_hr
## 7 Plan_Curvature crosc_DTM_50m_avg_ws7 maxic_DTM_50m_avg_ws7
## 8 planc_ws7_hr slope_ws29_hr slope_DTM_50m_avg_ws7
## 9 Convergence_Index slope_ws23_hr maxic_DTM_50m_avg_ws3
## 10 Longitudinal_Curvature Longitudinal_Curvature crosc_ws15
## k 4 k 5
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7
## 2 Convexity Convexity
## 3 profc_DTM_50m_avg_ws7 profc_DTM_50m_avg_ws7
## 4 maxic_DTM_50m_avg_ws5 minic_DTM_50m_avg_ws7
## 5 minic_DTM_50m_avg_ws7 slope_ws7
## 6 slope_ws7_hr crosc_DTM_50m_avg_ws5
## 7 planc_DTM_50m_avg_ws5 DiurnalAnisotropicHeating
## 8 maxic_ws15 Minimal_Curvature
## 9 Longitudinal_Curvature Convergence_Index
## 10 maxic_ws29_hr crosc_ws19_hr
## allchosen Freq
## 2 Convexity 5
## 25 slope_DTM_50m_avg_ws7 5
## 9 Longitudinal_Curvature 4
## 11 maxic_DTM_50m_avg_ws5 3
## 22 profc_DTM_50m_avg_ws7 3
## 1 Convergence_Index 2
## 4 crosc_DTM_50m_avg_ws7 2
## 15 minic_DTM_50m_avg_ws7 2
## 20 planc_ws7_hr 2
## 27 slope_ws29_hr 2
## 28 slope_ws7 2## [1] "10fold cv-error: 0.630091649694501 for predictors slope_DTM_50m_avg_ws7 AND Convexity AND profc_DTM_50m_avg_ws7 AND planc_DTM_50m_avg_ws7 AND maxic_DTM_50m_avg_ws5"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 28 2 0 4 0 4 0 0 0 2 6 1 3 0 0 3 4
## Ant 12 45 0 5 0 7 8 0 1 8 2 5 3 0 7 0 1
## CBD 0 0 134 0 20 0 0 2 26 10 0 0 21 7 62 25 9
## CD 7 2 0 30 0 3 9 0 2 1 8 3 7 5 4 9 4
## CSR 0 0 5 0 121 0 0 0 1 2 0 0 0 18 7 1 0
## DC 22 4 0 30 0 75 13 0 0 13 9 1 3 0 1 2 12
## GLD 25 78 0 64 1 68 279 51 2 111 62 4 72 28 11 34 63
## IMS 10 8 0 16 0 5 36 107 7 2 6 0 9 6 6 6 14
## ISR 0 0 29 0 6 0 0 7 92 3 1 0 22 6 26 23 26
## LD 0 2 3 0 2 0 0 0 0 12 0 0 0 2 2 0 0
## LT 0 6 0 10 0 5 38 0 0 7 82 0 5 20 2 8 29
## MrD 16 13 0 2 0 14 0 0 0 0 0 103 0 0 0 0 0
## MxD 1 4 1 5 0 6 4 2 11 2 6 0 29 1 6 2 5
## SB 4 9 1 3 10 1 2 4 3 0 6 0 0 85 6 6 3
## SD 0 0 6 1 0 1 1 6 4 9 0 0 5 5 26 5 0
## SSR 0 0 9 6 8 0 0 0 22 2 0 0 4 5 14 52 8
## TG 0 3 11 15 7 2 3 18 28 16 8 0 15 9 13 23 222
## WB 61 21 0 11 0 10 8 0 0 0 4 65 0 1 0 0 0
##
## preds WB
## AD 6
## Ant 8
## CBD 0
## CD 6
## CSR 0
## DC 3
## GLD 13
## IMS 3
## ISR 0
## LD 0
## LT 2
## MrD 42
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 118
## [1] "Kappa overall = 0.375059566470827"
## [1] "Tau overall = 0.383251467593147"
## [1] "mean quality = 0.252753790022417"
## [1] "The quality is 0.252753790022417"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.435516352305733"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 8 1 0 3 0 3 1 0 0 0 3 1 1 1 0 1
## Ant 10 24 0 5 0 4 3 0 0 2 2 3 1 4 5 0
## CBD 0 0 61 0 13 0 0 1 12 10 1 0 4 5 34 15
## CD 2 0 0 7 0 7 3 0 0 1 3 1 3 1 1 3
## CSR 0 0 3 0 66 0 0 0 1 2 0 0 0 14 4 0
## DC 11 3 0 14 0 41 7 0 0 6 4 0 4 1 0 1
## GLD 18 29 0 33 0 27 143 19 0 57 39 2 42 17 4 20
## IMS 8 6 3 8 0 3 13 56 0 2 0 0 4 3 5 7
## ISR 0 0 15 1 1 0 1 2 45 0 2 0 14 2 16 9
## LD 0 1 0 0 1 0 0 0 0 9 0 0 1 1 1 0
## LT 2 0 0 10 1 3 17 0 0 2 35 0 0 7 5 5
## MrD 4 10 0 1 0 8 0 0 0 0 0 46 0 0 0 0
## MxD 2 1 1 3 0 0 4 2 6 1 0 0 14 1 1 1
## SB 1 9 0 3 5 0 0 2 2 0 8 0 0 35 2 4
## SD 0 0 1 0 1 0 0 2 2 1 0 0 0 1 10 1
## SSR 1 0 6 2 3 0 0 0 12 0 0 0 3 4 3 25
## TG 1 1 11 3 0 1 4 13 20 7 3 0 9 3 9 8
## WB 23 14 0 8 0 3 4 0 0 0 0 31 0 0 0 0
##
## altpreds TG WB
## AD 2 1
## Ant 0 5
## CBD 5 0
## CD 4 2
## CSR 0 0
## DC 7 0
## GLD 30 4
## IMS 8 0
## ISR 12 0
## LD 0 0
## LT 11 1
## MrD 0 17
## MxD 2 0
## SB 11 2
## SD 0 0
## SSR 4 0
## TG 105 0
## WB 0 68
## [1] "classification error rate with altdata: 0.593893129770992"
## [1] "Kappa overall = 0.362834447626198"
## [1] "Tau overall = 0.371171980242479"
## [1] "mean quality = 0.239253882024838"
## [1] "The quality is 0.239253882024838"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.425124668256516"SVM n=200
## [1] "Prediction error at end is: 0.788630248277655"
## [2] "Prediction error at end is: 0.71928018978292"
## [3] "Prediction error at end is: 0.665479331860133"
## [4] "Prediction error at end is: 0.64941732744053"
## [5] "Prediction error at end is: 0.622390809827116"
## [6] "Prediction error at end is: 0.621111724944755"
## [7] "Prediction error at end is: 0.609894384505394"
## [8] "Prediction error at end is: 0.605299298063174"
## [9] "Prediction error at end is: 0.599186598206161"
## [10] "Prediction error at end is: 0.595619394254517"
## k 1 k 2
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7
## 2 Convexity Convexity
## 3 Longitudinal_Curvature Longitudinal_Curvature
## 4 slope_ws11 crosc_DTM_50m_avg_ws7
## 5 planc_DTM_50m_avg_ws7 slope_ws11
## 6 minic_DTM_50m_avg_ws7 maxic_DTM_50m_avg_ws7
## 7 maxic_ws15 profc_DTM_50m_avg_ws7
## 8 maxic_DTM_50m_avg_ws7 profc_ws29_hr
## 9 DiurnalAnisotropicHeating DiurnalAnisotropicHeating
## 10 Convergence_Index slope_ws11_hr
## k 3 k 4
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7
## 2 Convexity Convexity
## 3 Longitudinal_Curvature minic_DTM_50m_avg_ws7
## 4 slope_ws11 profc_DTM_50m_avg_ws7
## 5 minic_DTM_50m_avg_ws7 slope_ws23_hr
## 6 Convergence_Index maxic_DTM_50m_avg_ws7
## 7 DiurnalAnisotropicHeating DiurnalAnisotropicHeating
## 8 maxic_DTM_50m_avg_ws7 Convergence_Index
## 9 profc_DTM_50m_avg_ws7 planc_ws5_hr
## 10 slope_ws13_hr profc_DTM_50m_avg_ws5
## k 5
## 1 slope_DTM_50m_avg_ws7
## 2 Convexity
## 3 Longitudinal_Curvature
## 4 crosc_DTM_50m_avg_ws7
## 5 slope_ws7
## 6 maxic_DTM_50m_avg_ws7
## 7 profc_DTM_50m_avg_ws7
## 8 DiurnalAnisotropicHeating
## 9 Convergence_Index
## 10 planc_DTM_50m_avg_ws7
## allchosen Freq
## 2 Convexity 5
## 4 DiurnalAnisotropicHeating 5
## 6 maxic_DTM_50m_avg_ws7 5
## 14 slope_DTM_50m_avg_ws7 5
## 1 Convergence_Index 4
## 5 Longitudinal_Curvature 4
## 12 profc_DTM_50m_avg_ws7 4
## 8 minic_DTM_50m_avg_ws7 3
## 15 slope_ws11 3
## 3 crosc_DTM_50m_avg_ws7 2
## 9 planc_DTM_50m_avg_ws7 2## [1] "10fold cv-error: 0.635114503816794 for predictors slope_DTM_50m_avg_ws7 AND Convexity AND Longitudinal_Curvature AND slope_ws11 AND crosc_DTM_50m_avg_ws7"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 15 11 0 2 0 1 3 0 0 0 3 4 0 0 0 0 1
## Ant 3 10 0 2 0 1 2 2 0 0 0 1 0 3 1 1 1
## CBD 0 0 50 0 6 0 1 1 4 2 1 0 1 1 17 1 1
## CD 0 0 0 8 0 3 0 0 0 0 1 0 0 2 0 0 1
## CSR 0 0 2 0 66 0 0 0 0 3 0 0 0 5 4 1 0
## DC 12 7 0 21 0 39 4 1 0 10 3 1 4 0 0 3 6
## GLD 18 31 0 31 0 35 138 23 0 56 41 0 38 15 1 17 32
## IMS 5 9 0 9 0 2 17 42 0 0 0 0 5 2 4 3 4
## ISR 0 0 24 0 5 0 1 5 64 0 2 0 12 7 19 19 14
## LD 0 1 5 0 0 0 0 0 0 15 1 0 1 0 2 0 0
## LT 2 0 0 10 1 3 16 0 0 2 41 1 0 6 5 4 16
## MrD 3 6 0 0 0 6 0 0 0 0 0 35 0 0 0 0 0
## MxD 1 0 1 4 0 1 6 6 5 0 0 0 18 2 1 0 2
## SB 1 9 0 0 5 0 2 0 1 0 1 0 0 48 3 4 3
## SD 2 1 5 0 5 0 0 1 2 4 0 0 1 4 23 0 3
## SSR 0 0 5 0 2 0 0 2 8 0 2 0 2 1 7 38 3
## TG 3 2 9 6 1 2 3 14 16 8 3 0 18 4 13 9 114
## WB 26 12 0 8 0 7 7 0 0 0 1 42 0 0 0 0 0
##
## preds WB
## AD 5
## Ant 3
## CBD 0
## CD 3
## CSR 0
## DC 1
## GLD 1
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 9
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 78
## [1] "Kappa overall = 0.386451408409586"
## [1] "Tau overall = 0.394881005837449"
## [1] "mean quality = 0.264477222683818"
## [1] "The quality is 0.264477222683818"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.45414061945107"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 24 8 0 5 0 2 2 0 0 1 6 3 0 0 0 2
## Ant 2 17 0 5 0 4 6 3 0 3 1 2 2 1 1 1
## CBD 0 0 82 0 16 0 0 4 17 3 0 0 10 2 38 7
## CD 4 3 0 16 0 1 2 0 0 1 2 2 8 3 1 0
## CSR 1 2 7 1 109 0 0 0 0 3 0 0 1 17 12 0
## DC 26 9 0 44 0 60 17 2 0 7 4 0 7 1 1 6
## GLD 35 51 0 58 2 85 244 48 0 119 76 0 68 29 9 34
## IMS 9 15 0 13 0 7 45 72 3 5 3 0 5 2 12 7
## ISR 0 0 44 1 18 0 0 9 83 5 0 0 22 13 46 38
## LD 0 1 13 0 0 0 0 0 0 9 0 0 0 0 7 0
## LT 3 8 0 10 0 6 39 1 0 7 85 1 0 21 4 8
## MrD 9 11 0 1 0 8 0 0 0 0 0 76 0 0 0 0
## MxD 0 2 2 8 0 6 3 11 8 1 5 0 24 3 1 5
## SB 3 22 3 1 16 1 7 1 5 3 4 0 0 69 2 7
## SD 1 6 17 0 7 1 1 5 10 19 0 0 5 10 34 6
## SSR 0 0 12 2 3 1 0 4 31 1 3 0 6 12 8 55
## TG 3 10 19 22 4 4 12 37 42 13 10 0 39 14 17 23
## WB 66 32 0 15 0 15 23 0 0 0 1 98 1 1 0 0
##
## altpreds TG WB
## AD 1 26
## Ant 2 2
## CBD 9 0
## CD 2 2
## CSR 0 0
## DC 9 6
## GLD 72 6
## IMS 13 3
## ISR 26 0
## LD 0 0
## LT 27 2
## MrD 0 31
## MxD 6 0
## SB 4 0
## SD 6 0
## SSR 6 0
## TG 216 0
## WB 1 123
## [1] "classification error rate with altdata: 0.644093686354379"
## [1] "Kappa overall = 0.308333696989463"
## [1] "Tau overall = 0.318018449742422"
## [1] "mean quality = 0.203829530138025"
## [1] "The quality is 0.203829530138025"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.391547891288425"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=2,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of localterrain is 0.534740092640247"
## MeanDecreaseGini parameters
## slope_DTM_50m_avg_ws7 46.29627 slope_DTM_50m_avg_ws7
## slope_DTM_50m_avg_ws5 37.96943 slope_DTM_50m_avg_ws5
## Convexity 34.14706 Convexity
## slope_DTM_50m_avg_ws3 30.03132 slope_DTM_50m_avg_ws3
## slope_ws15 29.38625 slope_ws15
## Slope 26.60654 Slope
## slope_ws29_hr 25.83931 slope_ws29_hr
## slope_ws11 25.51571 slope_ws11
## slope_ws23_hr 24.41085 slope_ws23_hr
## slope_ws7 24.33957 slope_ws7
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 34 18 0 0 0 2 2 0 0 0 1 2 0 0 0 0
## Ant 8 63 0 3 2 2 1 0 0 2 2 6 0 2 3 0
## CBD 2 2 85 2 17 0 1 11 17 16 1 0 10 2 34 28
## CD 17 11 0 71 0 34 13 5 0 8 25 4 15 2 2 5
## CSR 0 0 9 0 108 0 0 0 8 5 0 0 0 41 9 4
## DC 22 4 0 29 0 104 8 0 0 7 12 4 5 0 0 1
## GLD 27 38 2 43 2 26 293 37 4 67 75 2 53 36 10 34
## IMS 8 8 2 9 1 4 17 98 1 5 2 0 1 4 6 3
## ISR 2 0 37 0 12 0 5 4 82 3 4 0 16 10 31 39
## LD 3 4 2 5 0 3 3 0 0 45 6 0 2 1 1 2
## LT 4 9 0 7 0 10 19 0 0 11 48 0 1 7 1 2
## MrD 44 8 0 3 0 2 4 0 0 0 0 148 0 0 0 0
## MxD 2 3 6 11 1 10 6 13 13 1 6 0 59 2 12 9
## SB 1 12 1 1 10 0 8 6 4 0 5 0 0 39 2 4
## SD 1 0 34 1 8 0 3 2 31 19 1 0 18 12 63 16
## SSR 1 1 4 0 8 0 2 3 14 1 1 0 3 8 5 41
## TG 4 13 17 16 5 3 14 17 25 10 11 0 15 32 11 11
## WB 2 2 0 0 0 0 1 0 0 0 0 9 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 2 2
## CBD 9 0
## CD 11 6
## CSR 0 0
## DC 10 0
## GLD 62 7
## IMS 6 1
## ISR 21 0
## LD 4 0
## LT 21 0
## MrD 0 7
## MxD 10 0
## SB 3 0
## SD 10 0
## SSR 1 0
## TG 230 0
## WB 0 166
## [1] "classification error rate with altdata: 0.543891170431211"
## [1] "Prediction error at end is: 0.721872565820221"
## [2] "Prediction error at end is: 0.642898686192034"
## [3] "Prediction error at end is: 0.582794568208963"
## [4] "Prediction error at end is: 0.545610687022901"
## [5] "Prediction error at end is: 0.532864672586592"
## [6] "Prediction error at end is: 0.500769849924703"
## [7] "Prediction error at end is: 0.524709196655762"
## [8] "Prediction error at end is: 0.501287843381627"
## [9] "Prediction error at end is: 0.494148880926416"
## [10] "Prediction error at end is: 0.487535701303422"
## k 1 k 2
## 1 slope_DTM_50m_avg_ws7 Slope
## 2 profc_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws5
## 3 Convexity Convexity
## 4 slope_ws29_hr Longitudinal_Curvature
## 5 profc_DTM_50m_avg_ws3 longc_DTM_50m_avg_ws3
## 6 minic_ws15 crosc_DTM_50m_avg_ws5
## 7 Slope slope_ws23_hr
## 8 CrossSectionalCurvature longc_DTM_50m_avg_ws7
## 9 Convergence_Index crosc_DTM_50m_avg_ws7
## 10 longc_DTM_50m_avg_ws5 slope_DTM_50m_avg_ws7
## k 3 k 4 k 5
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws3
## 2 profc_DTM_50m_avg_ws7 profc_DTM_50m_avg_ws7 longc_DTM_50m_avg_ws7
## 3 Convexity Convexity slope_DTM_50m_avg_ws7
## 4 Slope Slope Convexity
## 5 Total_Curvature crosc_DTM_50m_avg_ws7 Total_Curvature
## 6 crosc_DTM_50m_avg_ws7 Total_Curvature minic_DTM_50m_avg_ws5
## 7 planc_ws15 crosc_ws9_hr maxic_DTM_50m_avg_ws7
## 8 DiurnalAnisotropicHeating minic_ws11 slope_ws15_hr
## 9 planc_DTM_50m_avg_ws7 crosc_DTM_50m_avg_ws5 crosc_DTM_50m_avg_ws7
## 10 Longitudinal_Curvature Longitudinal_Curvature maxic_DTM_50m_avg_ws5
## allchosen Freq
## 2 Convexity 5
## 24 slope_DTM_50m_avg_ws7 5
## 4 crosc_DTM_50m_avg_ws7 4
## 21 Slope 4
## 11 Longitudinal_Curvature 3
## 20 profc_DTM_50m_avg_ws7 3
## 28 Total_Curvature 3
## 3 crosc_DTM_50m_avg_ws5 2
## 10 longc_DTM_50m_avg_ws7 2predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("slope_DTM_50m_avg_ws7","profc_DTM_50m_avg_ws7","Convexity","Slope","Total_Curvature","crosc_DTM_50m_avg_ws7"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.460050890585242 for predictors slope_DTM_50m_avg_ws7 AND profc_DTM_50m_avg_ws7 AND Convexity AND Slope AND Total_Curvature AND crosc_DTM_50m_avg_ws7"
## [1] "Kappa overall = 0.998375003445709"
## [1] "Tau overall = 0.998383475527616"
## [1] "mean quality = 0.996889954235226"
## [1] "The quality is 0.996889954235226"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.998359823983639"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 54 21 0 5 0 0 4 1 0 1 0 9 3 0 2 1
## Ant 13 102 0 6 1 7 12 1 0 3 6 10 3 7 4 1
## CBD 0 1 98 1 13 0 1 8 15 5 0 0 19 5 39 14
## CD 20 7 1 58 0 15 16 1 0 7 14 1 14 5 1 1
## CSR 0 0 12 0 122 0 0 0 4 3 0 0 0 17 8 2
## DC 21 1 0 20 0 115 11 0 1 7 4 1 5 2 0 1
## GLD 16 18 0 36 2 21 280 22 1 27 44 2 29 25 9 23
## IMS 8 10 1 12 0 2 15 140 5 0 4 0 5 5 5 2
## ISR 0 0 23 0 8 1 0 1 91 2 3 0 12 7 23 26
## LD 7 8 6 10 0 8 10 0 0 92 12 0 13 4 8 16
## LT 3 8 0 16 0 7 17 2 0 12 78 0 6 17 4 3
## MrD 29 2 0 2 0 0 1 0 0 0 0 123 0 0 0 0
## MxD 2 4 7 15 1 18 10 8 9 2 9 0 63 4 10 8
## SB 1 12 1 2 16 2 6 4 3 5 11 0 1 63 8 4
## SD 2 0 32 2 5 0 1 2 18 20 3 0 11 7 48 3
## SSR 1 0 9 9 5 0 5 4 28 1 2 0 9 14 10 86
## TG 6 3 9 8 2 5 10 3 24 13 10 0 5 15 12 8
## WB 3 0 0 0 0 0 2 0 0 0 0 36 0 1 2 0
##
## altpreds TG WB
## AD 0 0
## Ant 7 2
## CBD 9 0
## CD 9 8
## CSR 0 0
## DC 7 2
## GLD 28 6
## IMS 9 1
## ISR 20 0
## LD 10 1
## LT 32 2
## MrD 0 13
## MxD 15 0
## SB 8 0
## SD 4 0
## SSR 12 0
## TG 230 0
## WB 0 166
## [1] "classification error rate with altdata: 0.488543788187373"
## [1] "Kappa overall = 0.4788607578659"
## [1] "Tau overall = 0.482718341919252"
## [1] "mean quality = 0.346595046049029"
## [1] "The quality is 0.346595046049029"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.517494591625497"RandomForest n=200,
## [1] "Prediction error at end is: 0.666749317561419"
## [2] "Prediction error at end is: 0.600453334199922"
## [3] "Prediction error at end is: 0.522687833095021"
## [4] "Prediction error at end is: 0.48061484466398"
## [5] "Prediction error at end is: 0.463790458858703"
## [6] "Prediction error at end is: 0.457163330300273"
## [7] "Prediction error at end is: 0.439312686858183"
## [8] "Prediction error at end is: 0.441353503184713"
## [9] "Prediction error at end is: 0.446452944235019"
## [10] "Prediction error at end is: 0.439821591056805"
## k 1 k 2
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws3
## 2 longc_DTM_50m_avg_ws7 Longitudinal_Curvature
## 3 Convexity Convexity
## 4 CrossSectionalCurvature slope_DTM_50m_avg_ws7
## 5 slope_ws11 planc_DTM_50m_avg_ws7
## 6 crosc_DTM_50m_avg_ws5 longc_DTM_50m_avg_ws3
## 7 Longitudinal_Curvature profc_DTM_50m_avg_ws7
## 8 Slope CrossSectionalCurvature
## 9 Total_Curvature minic_DTM_50m_avg_ws5
## 10 profc_ws15 slope_ws23_hr
## k 3 k 4
## 1 slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws7
## 2 profc_DTM_50m_avg_ws7 Longitudinal_Curvature
## 3 Convexity Convexity
## 4 crosc_DTM_50m_avg_ws7 planc_DTM_50m_avg_ws7
## 5 slope_ws15 profc_DTM_50m_avg_ws7
## 6 Longitudinal_Curvature slope_ws7
## 7 longc_DTM_50m_avg_ws3 longc_DTM_50m_avg_ws3
## 8 DiurnalAnisotropicHeating Total_Curvature
## 9 minic_ws15 maxic_DTM_50m_avg_ws7
## 10 Total_Curvature DiurnalAnisotropicHeating
## k 5
## 1 slope_DTM_50m_avg_ws7
## 2 profc_DTM_50m_avg_ws7
## 3 Convexity
## 4 planc_DTM_50m_avg_ws7
## 5 slope_DTM_50m_avg_ws3
## 6 DiurnalAnisotropicHeating
## 7 profc_DTM_50m_avg_ws3
## 8 planc_DTM_50m_avg_ws5
## 9 minic_DTM_50m_avg_ws5
## 10 longc_ws29_hr
## allchosen Freq
## 1 Convexity 5
## 20 slope_DTM_50m_avg_ws7 5
## 9 Longitudinal_Curvature 4
## 16 profc_DTM_50m_avg_ws7 4
## 5 DiurnalAnisotropicHeating 3
## 6 longc_DTM_50m_avg_ws3 3
## 14 planc_DTM_50m_avg_ws7 3
## 25 Total_Curvature 3
## 4 CrossSectionalCurvature 2
## 11 minic_DTM_50m_avg_ws5 2
## 19 slope_DTM_50m_avg_ws3 2predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("slope_DTM_50m_avg_ws7","longc_DTM_50m_avg_ws7","Convexity","crosc_DTM_50m_avg_ws7","slope_ws11"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.428971486761711 for predictors slope_DTM_50m_avg_ws7 AND longc_DTM_50m_avg_ws7 AND Convexity AND crosc_DTM_50m_avg_ws7 AND slope_ws11"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"
## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 31 4 0 5 0 6 1 0 0 0 0 10 1 1 0 0
## Ant 7 68 0 4 0 3 1 3 0 1 1 2 4 4 3 2
## CBD 0 0 65 0 7 0 0 0 7 5 0 0 2 4 18 3
## CD 5 0 1 34 0 8 3 2 1 1 5 0 4 1 0 4
## CSR 0 0 4 0 67 0 0 0 1 2 0 0 0 11 6 1
## DC 7 1 0 11 0 60 4 1 0 5 1 1 5 1 0 2
## GLD 11 6 0 10 1 9 135 3 0 8 15 0 14 11 2 4
## IMS 5 3 2 5 0 1 5 81 0 0 1 0 5 0 3 3
## ISR 1 0 9 1 3 1 0 1 50 0 2 0 16 2 17 9
## LD 2 2 0 7 0 5 10 0 2 57 12 0 5 3 1 4
## LT 2 1 0 9 0 1 17 0 0 1 47 0 1 10 5 2
## MrD 10 8 0 2 0 2 3 0 0 0 0 65 0 0 0 0
## MxD 2 0 6 6 0 2 9 3 6 2 2 0 32 0 0 0
## SB 1 2 0 1 10 1 6 0 3 1 5 0 0 33 5 3
## SD 1 2 8 2 1 0 1 1 11 11 2 0 2 6 29 3
## SSR 0 0 2 1 1 0 1 0 10 3 1 0 5 6 8 58
## TG 2 1 4 3 1 1 4 2 9 3 6 0 4 7 3 2
## WB 4 1 0 0 0 0 0 0 0 0 0 6 0 0 0 0
##
## altpreds TG WB
## AD 0 4
## Ant 3 2
## CBD 3 0
## CD 6 2
## CSR 0 0
## DC 9 1
## GLD 14 0
## IMS 5 0
## ISR 13 0
## LD 9 0
## LT 8 1
## MrD 0 11
## MxD 3 0
## SB 12 1
## SD 1 0
## SSR 3 0
## TG 112 0
## WB 0 78
## [1] "classification error rate with altdata: 0.439185750636132"
## [1] "Kappa overall = 0.532471259772835"
## [1] "Tau overall = 0.534979793444095"
## [1] "mean quality = 0.394388336534084"
## [1] "The quality is 0.394388336534084"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.564347396552711"REGIONAL TERRAIN
SVM n=100
## [1] "Prediction error at end is: 0.78117048346056"
## [2] "Prediction error at end is: 0.702290076335878"
## [3] "Prediction error at end is: 0.615776081424936"
## [4] "Prediction error at end is: 0.590839694656489"
## [5] "Prediction error at end is: 0.56030534351145"
## [6] "Prediction error at end is: 0.548600508905852"
## [7] "Prediction error at end is: 0.550127226463104"
## [8] "Prediction error at end is: 0.550127226463104"
## [9] "Prediction error at end is: 0.553180661577608"
## [10] "Prediction error at end is: 0.550636132315522"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope sagaTopographic_Wetness_Index
## 3 Topographic_Wetness_Index Catchment_slope
## 4 Modified_Catchment_Area Topographic_Wetness_Index
## 5 LS_Factor LS_Factor
## 6 sagaTopographic_Wetness_Index Modified_Catchment_Area
## 7 MassBalanceIndex Mass_Balance_Index
## 8 Catchment_area Catchment_area
## 9 TWI LSFactor
## 10 LSFactor TWI
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Topographic_Wetness_Index Topographic_Wetness_Index
## 4 Modified_Catchment_Area LS_Factor
## 5 LS_Factor Modified_Catchment_Area
## 6 sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## 7 Mass_Balance_Index LSFactor
## 8 LSFactor Catchment_area
## 9 Catchment_area_hr Mass_Balance_Index
## 10 RelativeSlopePosition TWI
## k 5
## 1 Channel_Network_Base_Level
## 2 Catchment_slope
## 3 Topographic_Wetness_Index
## 4 LS_Factor
## 5 sagaTopographic_Wetness_Index
## 6 Modified_Catchment_Area
## 7 LSFactor
## 8 Mass_Balance_Index
## 9 Catchment_area
## 10 MassBalanceIndex
## allchosen Freq
## 3 Catchment_slope 5
## 4 Channel_Network_Base_Level 5
## 5 LSFactor 5
## 6 LS_Factor 5
## 9 Modified_Catchment_Area 5
## 11 sagaTopographic_Wetness_Index 5
## 12 Topographic_Wetness_Index 5
## 1 Catchment_area 4
## 8 Mass_Balance_Index 4
## 13 TWI 3
## 7 MassBalanceIndex 2## [1] "10fold cv-error: 0.54910941475827 for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND Modified_Catchment_Area AND LS_Factor AND sagaTopographic_Wetness_Index"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 31 13 0 2 0 0 0 0 0 0 0 18 0 0 0 0 0
## Ant 3 22 0 2 0 5 3 6 0 0 0 3 0 1 0 0 0
## CBD 0 0 58 0 9 0 0 2 22 2 1 0 12 4 20 0 4
## CD 5 0 0 15 0 1 2 1 0 0 2 1 2 2 0 0 0
## CSR 0 0 3 0 67 0 0 0 3 0 0 0 0 0 4 2 0
## DC 9 5 0 24 0 69 10 0 0 7 1 2 5 0 1 0 12
## GLD 23 36 0 27 0 9 141 54 0 1 21 1 8 16 3 8 5
## IMS 1 2 0 1 0 0 1 0 0 0 0 0 1 3 0 5 0
## ISR 0 0 17 0 5 0 0 0 46 1 0 0 11 1 20 8 9
## LD 1 1 0 6 0 0 4 0 0 59 16 0 1 16 8 16 24
## LT 2 2 0 5 0 1 27 6 0 0 49 0 1 8 1 3 8
## MrD 11 8 0 1 0 4 2 0 0 0 0 58 0 0 0 0 0
## MxD 1 4 2 3 0 3 0 0 4 0 0 0 27 1 1 0 5
## SB 0 1 0 0 0 0 1 4 2 0 1 0 0 30 6 0 6
## SD 0 0 2 0 0 0 0 0 1 0 0 0 0 0 10 0 0
## SSR 0 0 0 4 10 0 0 0 16 0 0 0 1 1 4 47 4
## TG 4 2 19 11 0 8 3 24 6 30 9 0 31 17 22 11 124
## WB 0 3 0 0 0 0 6 0 0 0 0 1 0 0 0 0 0
##
## preds WB
## AD 0
## Ant 2
## CBD 0
## CD 1
## CSR 0
## DC 2
## GLD 3
## IMS 0
## ISR 0
## LD 1
## LT 1
## MrD 1
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 89
## [1] "Kappa overall = 0.441499869140277"
## [1] "Tau overall = 0.448765154916929"
## [1] "mean quality = 0.315016139481741"
## [1] "The quality is 0.315016139481741"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.515444050043836"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 60 23 0 0 0 1 1 0 0 0 0 28 1 0 0 0
## Ant 13 49 0 3 0 3 6 11 0 1 1 3 1 0 0 0
## CBD 0 0 109 0 14 0 0 2 50 5 0 0 36 6 48 0
## CD 16 4 0 24 0 1 3 0 0 2 10 3 7 1 2 0
## CSR 0 0 3 0 115 0 0 0 8 0 0 0 0 1 7 8
## DC 13 16 0 48 0 131 14 1 0 9 4 5 8 0 3 1
## GLD 30 64 0 61 0 16 278 102 1 2 33 1 18 36 9 14
## IMS 2 1 0 1 0 0 12 0 0 0 1 0 0 7 0 3
## ISR 0 0 31 2 17 0 0 0 80 0 0 0 25 5 37 36
## LD 5 5 3 9 1 5 18 0 0 121 44 0 1 24 5 21
## LT 6 8 0 8 0 1 46 10 0 0 80 0 7 18 0 3
## MrD 27 6 0 4 0 6 3 1 0 0 0 131 1 0 0 0
## MxD 1 4 6 2 0 15 0 2 10 0 2 0 43 0 2 13
## SB 3 4 3 1 3 0 4 7 2 4 6 0 0 57 9 0
## SD 0 0 3 0 0 0 0 0 0 0 0 0 0 0 19 0
## SSR 0 0 1 7 25 0 0 0 34 0 0 0 11 4 8 77
## TG 10 8 40 32 0 22 4 61 14 56 19 0 39 38 44 23
## WB 0 5 0 0 0 0 12 0 0 0 0 11 0 1 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 17 0
## CD 4 6
## CSR 0 0
## DC 16 3
## GLD 16 8
## IMS 2 0
## ISR 26 0
## LD 48 5
## LT 13 0
## MrD 0 3
## MxD 2 0
## SB 7 0
## SD 0 0
## SSR 4 0
## TG 245 2
## WB 0 174
## [1] "classification error rate with altdata: 0.543533604887984"
## [1] "Kappa overall = 0.416936409742774"
## [1] "Tau overall = 0.424493830118606"
## [1] "mean quality = 0.29301003758555"
## [1] "The quality is 0.29301003758555"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.492911276640849"SVM n=200
## [1] "Prediction error at end is: 0.756875901525097"
## [2] "Prediction error at end is: 0.674394904458599"
## [3] "Prediction error at end is: 0.596236365699097"
## [4] "Prediction error at end is: 0.571034180969514"
## [5] "Prediction error at end is: 0.544303333819549"
## [6] "Prediction error at end is: 0.539974716779307"
## [7] "Prediction error at end is: 0.518845399588337"
## [8] "Prediction error at end is: 0.520370820570169"
## [9] "Prediction error at end is: 0.52495518711204"
## [10] "Prediction error at end is: 0.527245263447918"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Topographic_Wetness_Index Topographic_Wetness_Index
## 4 sagaTopographic_Wetness_Index Modified_Catchment_Area
## 5 LS_Factor LS_Factor
## 6 Mass_Balance_Index Mass_Balance_Index
## 7 Modified_Catchment_Area sagaTopographic_Wetness_Index
## 8 LSFactor TWI
## 9 Catchment_area LSFactor
## 10 TWI Catchment_area
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Topographic_Wetness_Index Topographic_Wetness_Index
## 4 Modified_Catchment_Area Modified_Catchment_Area
## 5 LS_Factor LS_Factor
## 6 sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## 7 Mass_Balance_Index Mass_Balance_Index
## 8 TWI TWI
## 9 LSFactor Catchment_Area2
## 10 Catchment_area_hr LSFactor
## k 5
## 1 Channel_Network_Base_Level
## 2 Catchment_slope
## 3 Topographic_Wetness_Index
## 4 Modified_Catchment_Area
## 5 LS_Factor
## 6 sagaTopographic_Wetness_Index
## 7 Mass_Balance_Index
## 8 TWI
## 9 LSFactor
## 10 Catchment_Area2
## allchosen Freq
## 4 Catchment_slope 5
## 5 Channel_Network_Base_Level 5
## 6 LSFactor 5
## 7 LS_Factor 5
## 8 Mass_Balance_Index 5
## 9 Modified_Catchment_Area 5
## 10 sagaTopographic_Wetness_Index 5
## 11 Topographic_Wetness_Index 5
## 12 TWI 5
## 1 Catchment_area 2
## 2 Catchment_Area2 2## [1] "10fold cv-error: 0.520366598778004 for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND Modified_Catchment_Area AND LS_Factor AND sagaTopographic_Wetness_Index"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 66 23 0 0 0 0 2 0 0 0 0 21 2 0 1 0 0
## Ant 6 46 0 0 0 2 1 4 0 3 1 2 1 0 0 0 0
## CBD 0 0 87 1 13 0 0 0 39 7 0 0 30 6 42 0 13
## CD 18 5 0 33 0 3 3 1 0 2 8 2 6 0 2 0 5
## CSR 0 0 3 0 118 0 0 0 8 0 0 0 1 1 7 10 0
## DC 11 21 0 47 0 143 11 1 1 11 4 2 11 0 4 7 20
## GLD 26 71 0 54 0 18 298 95 1 0 31 0 14 41 9 18 18
## IMS 6 2 0 3 0 2 8 43 3 0 1 0 3 6 6 3 6
## ISR 0 0 47 3 20 0 0 0 97 0 1 0 17 4 36 20 26
## LD 8 4 4 8 0 4 5 2 0 131 49 0 1 29 7 5 49
## LT 9 10 0 14 0 0 49 10 0 0 85 0 8 19 1 6 11
## MrD 30 10 0 4 0 7 7 1 0 0 0 154 1 0 0 0 0
## MxD 1 2 9 0 0 8 1 1 12 0 1 0 57 1 5 10 5
## SB 1 1 12 1 2 0 3 10 2 3 5 0 0 73 15 0 13
## SD 0 0 1 0 1 0 0 0 1 0 0 0 0 0 21 0 0
## SSR 0 0 4 18 19 5 0 0 28 0 0 0 20 7 8 101 11
## TG 4 1 32 16 2 9 2 29 7 43 14 0 26 10 29 19 223
## WB 0 1 0 0 0 0 11 0 0 0 0 1 0 1 0 0 0
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 5
## CSR 0
## DC 4
## GLD 4
## IMS 1
## ISR 0
## LD 4
## LT 0
## MrD 4
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 1
## WB 178
## [1] "Kappa overall = 0.462295688579045"
## [1] "Tau overall = 0.467892656044088"
## [1] "mean quality = 0.331750130769393"
## [1] "The quality is 0.331750130769393"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.523429876005305"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 29 13 0 3 0 1 0 0 0 0 0 19 0 0 0 0
## Ant 1 22 0 0 0 4 0 1 0 1 0 2 0 0 2 0
## CBD 0 0 48 0 10 0 0 1 19 2 1 0 7 2 18 0
## CD 4 0 0 18 0 4 2 0 0 1 3 1 5 2 0 0
## CSR 0 0 3 0 67 0 0 0 3 0 0 0 0 0 4 2
## DC 10 9 0 24 0 70 10 0 1 8 2 2 10 0 1 1
## GLD 23 39 0 29 0 10 136 50 0 1 23 0 6 15 3 12
## IMS 2 1 0 2 0 0 4 15 1 0 2 0 4 6 1 2
## ISR 0 0 23 0 5 0 0 0 48 1 1 0 9 2 17 8
## LD 4 1 1 4 0 0 2 0 0 61 15 0 2 17 9 7
## LT 1 1 0 5 0 1 33 9 0 0 47 0 2 11 1 6
## MrD 15 9 0 1 0 4 5 0 0 0 0 59 0 0 0 0
## MxD 1 2 5 2 0 3 1 1 5 0 0 0 26 2 2 0
## SB 0 0 1 0 0 0 0 5 2 1 1 0 1 36 11 0
## SD 0 0 1 0 1 0 0 0 1 0 0 0 0 0 13 0
## SSR 0 0 3 8 8 1 0 0 15 0 0 0 8 2 5 48
## TG 1 0 16 5 0 2 3 15 5 24 5 0 20 5 13 14
## WB 0 2 0 0 0 0 4 0 0 0 0 1 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 4 0
## CD 0 0
## CSR 0 0
## DC 12 3
## GLD 6 5
## IMS 3 0
## ISR 12 0
## LD 25 1
## LT 6 1
## MrD 0 2
## MxD 5 0
## SB 13 0
## SD 0 0
## SSR 7 0
## TG 108 0
## WB 0 88
## [1] "classification error rate with altdata: 0.522137404580153"
## [1] "Kappa overall = 0.441203576026597"
## [1] "Tau overall = 0.447148630444544"
## [1] "mean quality = 0.317958141141582"
## [1] "The quality is 0.317958141141582"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.512285026855902"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=3,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of regionalterrain is 0.363423212192263"
## MeanDecreaseGini
## Channel_Network_Base_Level 207.87310
## Modified_Catchment_Area 134.11583
## Catchment_slope 124.26100
## Protection_Index 120.13880
## LS_Factor 109.43648
## sagaTopographic_Wetness_Index 104.55499
## RelativeSlopePosition 89.92425
## Topographic_Wetness_Index 85.32774
## LSFactor 84.57299
## Mass_Balance_Index 78.86478
## parameters
## Channel_Network_Base_Level Channel_Network_Base_Level
## Modified_Catchment_Area Modified_Catchment_Area
## Catchment_slope Catchment_slope
## Protection_Index Protection_Index
## LS_Factor LS_Factor
## sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## RelativeSlopePosition RelativeSlopePosition
## Topographic_Wetness_Index Topographic_Wetness_Index
## LSFactor LSFactor
## Mass_Balance_Index Mass_Balance_Index
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 66 12 0 2 0 2 0 0 0 0 1 1 4 0 0 0
## Ant 19 120 1 0 0 4 7 5 0 0 0 12 0 7 0 1
## CBD 0 0 58 0 1 0 0 0 12 3 0 0 11 3 8 2
## CD 8 7 0 64 0 12 11 0 0 0 7 0 5 0 1 1
## CSR 0 0 1 0 1 0 0 0 2 0 0 0 0 0 0 0
## DC 23 1 0 35 0 128 5 1 0 5 3 3 7 0 2 0
## GLD 13 19 0 31 0 12 309 23 0 1 24 3 11 14 3 2
## IMS 6 4 0 8 0 0 20 127 0 0 2 0 2 12 6 8
## ISR 0 0 18 2 11 0 0 0 116 1 0 0 17 3 18 18
## LD 2 3 10 5 0 3 1 0 0 149 13 0 0 17 10 0
## LT 6 5 0 10 0 3 9 0 1 0 98 0 3 12 1 0
## MrD 26 2 0 0 0 2 3 0 0 0 0 160 0 0 0 0
## MxD 2 4 7 7 0 14 0 4 8 0 9 0 90 5 9 18
## SB 3 6 9 0 3 1 8 6 3 7 20 0 0 90 15 0
## SD 0 1 7 1 2 0 0 0 6 7 0 0 0 11 30 1
## SSR 0 0 1 9 4 1 2 0 18 1 1 0 9 11 9 127
## TG 5 1 10 12 0 6 1 18 4 12 11 0 4 3 14 5
## WB 0 1 0 2 0 0 2 0 0 1 0 1 0 1 1 0
##
## altpreds TG WB
## AD 0 1
## Ant 0 0
## CBD 8 0
## CD 8 0
## CSR 0 0
## DC 13 0
## GLD 15 6
## IMS 6 0
## ISR 23 0
## LD 17 0
## LT 14 0
## MrD 0 0
## MxD 15 0
## SB 9 0
## SD 0 0
## SSR 8 0
## TG 237 3
## WB 0 191
## [1] "classification error rate with altdata: 0.366089762393664"
## [1] "Prediction error at end is: 0.660559796437659"
## [2] "Prediction error at end is: 0.524681933842239"
## [3] "Prediction error at end is: 0.437150127226463"
## [4] "Prediction error at end is: 0.412213740458015"
## [5] "Prediction error at end is: 0.385750636132316"
## [6] "Prediction error at end is: 0.38676844783715"
## [7] "Prediction error at end is: 0.388295165394402"
## [8] "Prediction error at end is: 0.391857506361323"
## [9] "Prediction error at end is: 0.387786259541985"
## [10] "Prediction error at end is: 0.390330788804071"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Modified_Catchment_Area Topographic_Wetness_Index
## 4 LS_Factor RelativeSlopePosition
## 5 Topographic_Wetness_Index LS_Factor
## 6 Mass_Balance_Index Modified_Catchment_Area
## 7 RelativeSlopePosition Mass_Balance_Index
## 8 sagaTopographic_Wetness_Index Catchment_area_hr
## 9 TWI Catchment_Area2
## 10 Catchment_area MassBalanceIndex
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Topographic_Wetness_Index Modified_Catchment_Area
## 4 RelativeSlopePosition LS_Factor
## 5 LS_Factor RelativeSlopePosition
## 6 Modified_Catchment_Area Topographic_Wetness_Index
## 7 Mass_Balance_Index LSFactor
## 8 LSFactor MassBalanceIndex
## 9 MassBalanceIndex Catchment_Area2
## 10 sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## k 5
## 1 Channel_Network_Base_Level
## 2 Catchment_slope
## 3 Modified_Catchment_Area
## 4 LS_Factor
## 5 RelativeSlopePosition
## 6 TWI
## 7 Catchment_area_hr
## 8 Mass_Balance_Index
## 9 Catchment_Area2
## 10 sagaTopographic_Wetness_Index
## allchosen Freq
## 4 Catchment_slope 5
## 5 Channel_Network_Base_Level 5
## 7 LS_Factor 5
## 10 Modified_Catchment_Area 5
## 11 RelativeSlopePosition 5
## 9 Mass_Balance_Index 4
## 12 sagaTopographic_Wetness_Index 4
## 13 Topographic_Wetness_Index 4
## 2 Catchment_Area2 3
## 8 MassBalanceIndex 3
## 3 Catchment_area_hr 2
## 6 LSFactor 2
## 14 TWI 2predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","Catchment_slope","Topographic_Wetness_Index","RelativeSlopePosition","LS_Factor"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.361323155216285 for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND RelativeSlopePosition AND LS_Factor"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 70 13 0 11 0 1 8 2 0 0 5 12 4 0 1 0
## Ant 13 134 0 4 0 7 3 2 0 2 3 13 1 8 1 0
## CBD 1 0 127 1 7 0 0 1 13 1 0 0 18 7 24 1
## CD 15 10 0 81 0 12 20 1 0 2 10 0 9 2 2 1
## CSR 0 0 5 0 126 0 0 0 13 0 0 0 2 1 8 5
## DC 13 5 0 31 0 123 4 1 0 2 6 3 7 0 1 3
## GLD 17 17 0 23 0 9 314 23 0 0 19 5 8 17 4 4
## IMS 1 1 1 9 0 0 17 149 0 0 3 0 2 14 6 3
## ISR 0 0 9 0 22 2 0 0 129 0 0 0 16 6 37 17
## LD 2 4 6 8 0 4 1 0 0 152 11 0 0 12 9 0
## LT 11 5 0 2 0 2 11 0 0 4 95 0 2 17 3 1
## MrD 33 2 0 0 0 3 2 0 0 0 0 148 0 0 0 0
## MxD 2 2 10 5 0 15 1 3 16 3 3 0 98 2 8 9
## SB 1 2 13 2 2 1 9 7 2 4 12 0 1 81 15 4
## SD 0 1 13 2 6 0 0 3 6 9 3 0 10 9 59 0
## SSR 0 1 0 6 12 3 3 1 13 1 1 0 9 10 6 142
## TG 7 0 15 17 0 19 4 4 7 20 29 0 11 11 9 9
## WB 0 0 0 0 0 0 4 0 0 0 0 1 0 1 0 0
##
## altpreds TG WB
## AD 2 2
## Ant 1 1
## CBD 4 0
## CD 10 5
## CSR 0 0
## DC 13 3
## GLD 10 9
## IMS 3 0
## ISR 28 0
## LD 23 1
## LT 11 1
## MrD 0 1
## MxD 21 0
## SB 13 0
## SD 3 0
## SSR 3 0
## TG 255 1
## WB 0 177
## [1] "classification error rate with altdata: 0.373727087576375"
## [1] "Kappa overall = 0.601655549969939"
## [1] "Tau overall = 0.604288966095603"
## [1] "mean quality = 0.461701262064355"
## [1] "The quality is 0.461701262064355"
## [1] "######### Cramer's V = 0.625758372970374"RandomForest n=200
## [1] "Prediction error at end is: 0.623474497982205"
## [2] "Prediction error at end is: 0.482946143498485"
## [3] "Prediction error at end is: 0.385441402894605"
## [4] "Prediction error at end is: 0.35565549991086"
## [5] "Prediction error at end is: 0.328666958396136"
## [6] "Prediction error at end is: 0.318741025267013"
## [7] "Prediction error at end is: 0.322307904247905"
## [8] "Prediction error at end is: 0.324597332296073"
## [9] "Prediction error at end is: 0.325613199137777"
## [10] "Prediction error at end is: 0.324851785222282"
## k 1 k 2
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Topographic_Wetness_Index Modified_Catchment_Area
## 4 RelativeSlopePosition RelativeSlopePosition
## 5 LS_Factor LS_Factor
## 6 Modified_Catchment_Area Catchment_Area2
## 7 Mass_Balance_Index Mass_Balance_Index
## 8 MassBalanceIndex TWI
## 9 Catchment_area_hr Catchment_area_hr
## 10 Catchment_area sagaTopographic_Wetness_Index
## k 3 k 4
## 1 Channel_Network_Base_Level Channel_Network_Base_Level
## 2 Catchment_slope Catchment_slope
## 3 Modified_Catchment_Area Topographic_Wetness_Index
## 4 RelativeSlopePosition LS_Factor
## 5 LS_Factor RelativeSlopePosition
## 6 Mass_Balance_Index sagaTopographic_Wetness_Index
## 7 Catchment_Area2 Modified_Catchment_Area
## 8 LSFactor MassBalanceIndex
## 9 Catchment_area Mass_Balance_Index
## 10 sagaTopographic_Wetness_Index LSFactor
## k 5
## 1 Channel_Network_Base_Level
## 2 Catchment_slope
## 3 Modified_Catchment_Area
## 4 LS_Factor
## 5 RelativeSlopePosition
## 6 Catchment_area
## 7 Mass_Balance_Index
## 8 LSFactor
## 9 Topographic_Wetness_Index
## 10 Catchment_Area2
## allchosen Freq
## 4 Catchment_slope 5
## 5 Channel_Network_Base_Level 5
## 7 LS_Factor 5
## 9 Mass_Balance_Index 5
## 10 Modified_Catchment_Area 5
## 11 RelativeSlopePosition 5
## 1 Catchment_area 3
## 2 Catchment_Area2 3
## 6 LSFactor 3
## 12 sagaTopographic_Wetness_Index 3
## 13 Topographic_Wetness_Index 3
## 3 Catchment_area_hr 2
## 8 MassBalanceIndex 2predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","Catchment_slope","Topographic_Wetness_Index","RelativeSlopePosition","LS_Factor"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.307281059063136 for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND RelativeSlopePosition AND LS_Factor"
## [1] "Kappa overall = 0.999458070517194"
## [1] "Tau overall = 0.999460884149994"
## [1] "mean quality = 0.999154478701509"
## [1] "The quality is 0.999154478701509"
## [1] "######### Cramer's V = 0.999553021648355"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 39 3 0 5 0 8 2 0 0 0 4 9 0 0 0 0
## Ant 5 86 0 4 0 1 1 3 0 0 1 2 0 4 0 2
## CBD 0 0 69 0 5 0 0 0 6 0 0 0 6 3 13 0
## CD 11 0 0 47 0 11 6 2 0 1 5 0 3 1 1 1
## CSR 0 0 5 0 77 0 0 0 9 0 0 0 1 3 3 2
## DC 0 2 0 7 0 69 5 0 0 4 3 2 3 1 2 0
## GLD 7 1 0 11 0 4 167 9 0 0 7 0 4 7 0 7
## IMS 1 1 1 5 0 0 6 81 1 0 4 0 2 3 2 0
## ISR 0 0 2 0 3 0 0 0 66 0 0 0 3 1 10 7
## LD 0 0 0 4 0 0 1 0 0 82 5 0 0 6 8 0
## LT 4 2 0 6 0 1 2 0 0 1 57 0 2 9 1 0
## MrD 13 4 0 0 0 1 3 0 0 0 0 70 0 0 0 0
## MxD 4 0 10 5 0 3 2 1 6 1 0 0 61 2 5 0
## SB 1 0 2 0 0 0 1 1 0 4 7 0 2 44 11 2
## SD 0 0 7 0 5 0 1 0 6 3 2 0 5 7 35 0
## SSR 0 0 0 4 1 2 0 0 3 0 0 0 1 2 2 78
## TG 3 0 5 3 0 0 2 0 3 4 5 0 7 5 7 1
## WB 3 0 0 0 0 0 1 0 0 0 0 1 0 2 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 1 0
## CD 4 0
## CSR 0 0
## DC 12 0
## GLD 5 1
## IMS 3 0
## ISR 10 0
## LD 3 0
## LT 5 1
## MrD 0 1
## MxD 8 0
## SB 5 1
## SD 2 0
## SSR 3 0
## TG 140 0
## WB 0 96
## [1] "classification error rate with altdata: 0.305852417302799"
## [1] "Kappa overall = 0.674331995416797"
## [1] "Tau overall = 0.67615626403233"
## [1] "mean quality = 0.535103192109971"
## [1] "The quality is 0.535103192109971"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.68988604045021"ROUGHNESS
SVM n=100
## [1] "Prediction error at end is: 0.801237212442229"
## [2] "Prediction error at end is: 0.713558705925118"
## [3] "Prediction error at end is: 0.663105623928961"
## [4] "Prediction error at end is: 0.646815443734746"
## [5] "Prediction error at end is: 0.642219712312406"
## [6] "Prediction error at end is: 0.639155891364179"
## [7] "Prediction error at end is: 0.645277042114556"
## [8] "Prediction error at end is: 0.638648283740977"
## [9] "Prediction error at end is: 0.637631770265358"
## [10] "Prediction error at end is: 0.637626577348497"
## k 1 k 2
## 1 TRI_hr_ws26 TRI_hr_ws22
## 2 Texture Texture
## 3 fischerk_ws59 fischerk_ws59
## 4 terraintexture_hr_ws37_tp25 terraintexture_hr_ws57_tp25
## 5 vectorruggedness_hr_ws49 vectorruggedness_hr_ws25
## 6 vectorruggedness_hr_ws59 TRI_hr_ws26
## 7 terraintexture_hr_ws57_tp25 fischerk_ws31
## 8 vectorruggedness_hr_ws15 terraintexture_hr_ws13_tp5
## 9 TRI_hr_ws24 terraintexture_hr_ws21_tp25
## 10 TRI_hr_ws15 TRI_hr_ws24
## k 3 k 4
## 1 TRI_hr_ws26 TRI_hr_ws24
## 2 Texture Texture
## 3 fischerk_ws43 fischerk_ws59
## 4 terraintexture_hr_ws45_tp25 terraintexture_hr_ws53_tp25
## 5 vectorruggedness_hr_ws59 vectorruggedness_hr_ws19
## 6 fischerk_ws21 vectorruggedness_hr_ws53
## 7 fischerk_ws55 vectorstrength_hr_ws59
## 8 vectorruggedness_hr_ws17 fischerk_ws9
## 9 vectorruggedness_hr_ws53 TRI_hr_ws26
## 10 terraintexture_hr_ws41_tp5 vectorruggedness_hr_ws59
## k 5
## 1 TRI_hr_ws26
## 2 Texture
## 3 fischerk_ws33
## 4 terraintexture_hr_ws57_tp25
## 5 vectorruggedness_hr_ws59
## 6 vectorruggedness_hr_ws15
## 7 fischerk_ws7
## 8 vectorruggedness_hr_ws47
## 9 TRI_hr_ws18
## 10 TRI_hr_ws25
## allchosen Freq
## 16 Texture 5
## 22 TRI_hr_ws26 5
## 30 vectorruggedness_hr_ws59 4
## 6 fischerk_ws59 3
## 15 terraintexture_hr_ws57_tp25 3
## 20 TRI_hr_ws24 3
## 23 vectorruggedness_hr_ws15 2
## 29 vectorruggedness_hr_ws53 2## [1] "10fold cv-error: 0.623343527013252 for predictors TRI_hr_ws26 AND Texture AND fischerk_ws59 AND terraintexture_hr_ws57_tp25 AND vectorruggedness_hr_ws59"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 14 4 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0
## Ant 3 21 0 1 1 1 5 2 0 1 0 0 0 7 0 2 4
## CBD 3 0 57 1 9 0 0 4 19 11 0 0 9 7 26 10 3
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 8 0 70 0 0 0 3 4 0 0 0 14 4 2 1
## DC 2 0 0 10 0 56 1 7 0 1 2 4 2 0 0 0 6
## GLD 35 34 0 67 2 33 156 45 5 22 84 5 50 27 14 18 55
## IMS 1 1 6 4 1 0 1 25 0 0 1 0 5 3 2 2 5
## ISR 0 1 5 3 2 0 4 2 40 1 0 0 7 4 9 5 4
## LD 1 8 0 4 0 0 3 1 0 44 2 0 1 1 1 3 4
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 8 14 0 7 0 3 9 0 0 3 3 32 0 1 0 0 1
## MxD 1 3 3 1 0 1 0 6 4 1 1 0 12 1 3 2 6
## SB 1 0 0 0 1 0 3 0 1 1 0 0 0 17 2 5 0
## SD 2 1 3 0 2 0 1 0 5 5 3 0 4 8 29 5 3
## SSR 0 0 1 2 3 1 7 3 12 0 1 0 1 8 4 45 2
## TG 2 2 18 1 0 5 4 2 11 6 1 0 9 2 6 1 106
## WB 17 10 0 0 0 0 6 0 0 0 2 38 0 0 0 0 0
##
## preds WB
## AD 2
## Ant 1
## CBD 0
## CD 0
## CSR 0
## DC 0
## GLD 5
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 4
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 88
## [1] "Kappa overall = 0.368450928779918"
## [1] "Tau overall = 0.37938478143551"
## [1] "mean quality = 0.248452555581233"
## [1] "The quality is 0.248452555581233"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 31 12 0 0 0 0 0 0 0 0 0 18 0 5 0 0
## Ant 2 37 0 1 1 2 8 4 1 5 6 0 0 7 9 4
## CBD 4 0 110 0 25 0 0 4 42 24 1 0 20 13 63 10
## CD 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## CSR 0 0 14 0 96 0 0 0 6 3 0 0 0 41 9 4
## DC 15 0 0 13 0 115 13 6 0 4 6 5 3 0 2 0
## GLD 58 86 4 137 4 63 283 98 8 48 141 21 92 43 19 23
## IMS 6 2 11 5 1 1 6 48 3 2 5 0 7 18 6 15
## ISR 0 2 14 7 12 1 21 3 60 0 3 0 24 9 17 15
## LD 5 8 0 5 1 4 8 4 0 82 7 0 1 5 1 8
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 22 14 0 19 0 3 25 0 0 2 11 63 0 1 0 0
## MxD 1 4 7 5 0 3 4 9 16 1 2 0 22 4 7 5
## SB 3 0 1 1 8 0 1 0 2 3 0 0 0 13 1 5
## SD 0 2 1 1 5 0 2 1 20 11 1 0 3 12 34 28
## SSR 3 3 0 2 17 0 11 12 30 2 1 0 8 17 10 70
## TG 0 4 37 4 3 8 6 7 11 12 7 0 18 5 13 11
## WB 36 23 0 2 0 0 13 1 0 0 9 70 0 5 0 1
##
## altpreds TG WB
## AD 0 2
## Ant 6 2
## CBD 17 0
## CD 0 0
## CSR 0 0
## DC 7 0
## GLD 105 10
## IMS 8 0
## ISR 10 0
## LD 7 1
## LT 0 0
## MrD 4 16
## MxD 9 0
## SB 0 0
## SD 2 0
## SSR 10 0
## TG 214 0
## WB 1 170
## [1] "classification error rate with altdata: 0.630423685553854"
## [1] "Kappa overall = 0.321238957418729"
## [1] "Tau overall = 0.332492568237096"
## [1] "mean quality = 0.2088147732122"
## [1] "The quality is 0.2088147732122"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"SVM n=200
## [1] "Prediction error at end is: 0.81622911616754"
## [2] "Prediction error at end is: 0.703674055829228"
## [3] "Prediction error at end is: 0.665644955300128"
## [4] "Prediction error at end is: 0.644461060286183"
## [5] "Prediction error at end is: 0.628894297182474"
## [6] "Prediction error at end is: 0.633229728673078"
## [7] "Prediction error at end is: 0.635529556650246"
## [8] "Prediction error at end is: 0.633230706075534"
## [9] "Prediction error at end is: 0.632206388302447"
## [10] "Prediction error at end is: 0.633994708994709"
## k 1 k 2
## 1 TRI_hr_ws26 TRI_hr_ws25
## 2 Texture Texture
## 3 fischerk_ws59 fischerk_ws45
## 4 vectorruggedness_hr_ws57 vectorruggedness_hr_ws59
## 5 terraintexture_hr_ws57_tp25 terraintexture_hr_ws57_tp25
## 6 TRI_hr_ws2 fischerk_ws19
## 7 fischerk_ws31 vectorruggedness_hr_ws43
## 8 vectorruggedness_hr_ws35 TRI_hr_ws13
## 9 terraintexture_hr_ws53_tp25 fischerk_ws55
## 10 vectorruggedness_hr_ws59 fischerk_ws11
## k 3 k 4
## 1 TRI_hr_ws25 Texture
## 2 Texture TRI_hr_ws26
## 3 fischerk_ws61 fischerk_ws53
## 4 terraintexture_hr_ws57_tp25 terraintexture_hr_ws57_tp25
## 5 vectorruggedness_hr_ws57 vectorruggedness_hr_ws43
## 6 fischerk_ws29 terraintexture_hr_ws45_tp5
## 7 terraintexture_hr_ws41_tp5 vectorruggedness_hr_ws53
## 8 vectorruggedness_hr_ws49 fischerk_ws7
## 9 TRI_hr_ws19 TRI_hr_ws11
## 10 TRI_hr_ws21 fischerk_ws61
## k 5
## 1 TRI_hr_ws26
## 2 Texture
## 3 fischerk_ws57
## 4 terraintexture_hr_ws57_tp25
## 5 vectorruggedness_hr_ws57
## 6 terraintexture_hr_ws49_tp5
## 7 fischerk_ws7
## 8 terraintexture_hr_ws49_tp25
## 9 vectorruggedness_hr_ws59
## 10 vectorstrength_hr_ws57
## allchosen Freq
## 17 terraintexture_hr_ws57_tp25 5
## 18 Texture 5
## 25 TRI_hr_ws26 3
## 30 vectorruggedness_hr_ws57 3
## 31 vectorruggedness_hr_ws59 3
## 10 fischerk_ws61 2
## 11 fischerk_ws7 2
## 24 TRI_hr_ws25 2
## 27 vectorruggedness_hr_ws43 2## [1] "10fold cv-error: 0.619703930576825 for predictors TRI_hr_ws26 AND Texture AND fischerk_ws59 AND terraintexture_hr_ws57_tp25 AND vectorruggedness_hr_ws59"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 49 2 0 1 0 0 2 0 0 0 0 26 0 6 0 0 0
## Ant 3 59 0 2 3 2 13 2 0 1 7 2 0 10 7 1 7
## CBD 4 0 117 1 25 0 0 3 37 25 1 0 26 13 63 14 11
## CD 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## CSR 0 0 6 0 104 0 0 0 4 2 0 0 0 34 10 2 0
## DC 21 0 1 28 0 120 16 7 0 4 10 3 4 0 1 0 13
## GLD 47 71 4 123 5 54 268 97 5 38 125 14 84 36 20 14 99
## IMS 13 7 11 7 1 1 15 70 12 6 7 0 18 15 8 11 13
## ISR 0 1 14 11 10 1 18 2 83 1 4 0 24 7 15 22 16
## LD 6 13 0 4 1 5 12 0 0 99 12 0 2 7 2 12 9
## LT 0 3 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## MrD 17 19 0 15 0 7 28 0 0 1 13 82 0 1 0 0 3
## MxD 0 3 9 1 0 1 2 1 5 2 1 0 14 3 6 1 3
## SB 2 0 1 2 5 0 1 1 2 1 1 0 2 28 4 7 0
## SD 1 0 4 0 1 0 1 1 12 9 2 0 4 13 28 11 2
## SSR 1 3 1 2 16 0 9 10 22 3 3 0 4 15 15 94 12
## TG 1 2 31 2 2 9 4 3 17 8 3 0 16 6 12 10 210
## WB 21 14 0 2 0 0 12 0 0 0 8 50 0 4 0 0 2
##
## preds WB
## AD 7
## Ant 0
## CBD 0
## CD 0
## CSR 0
## DC 0
## GLD 11
## IMS 0
## ISR 0
## LD 2
## LT 0
## MrD 20
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 161
## [1] "Kappa overall = 0.361383942396621"
## [1] "Tau overall = 0.370597243491577"
## [1] "mean quality = 0.242001039865342"
## [1] "The quality is 0.242001039865342"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.428566107151365"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 21 4 0 0 0 0 1 0 0 0 0 10 0 0 0 0
## Ant 4 32 0 3 1 2 10 2 0 0 3 1 0 6 1 0
## CBD 2 0 59 1 10 0 0 3 25 12 2 0 11 7 31 13
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 8 0 64 0 0 0 3 3 0 0 0 20 4 1
## DC 5 0 0 14 0 60 5 7 0 1 6 4 2 0 0 0
## GLD 31 26 0 63 2 29 136 43 3 20 77 4 48 23 11 17
## IMS 1 1 6 3 1 0 7 33 6 1 1 0 9 6 5 3
## ISR 0 0 7 2 4 0 5 1 33 1 0 0 9 7 11 10
## LD 2 9 0 2 0 2 7 1 0 49 2 0 1 2 1 4
## LT 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 10 13 0 9 0 1 10 0 0 0 2 38 0 1 0 0
## MxD 0 1 5 1 0 0 0 2 3 1 0 0 5 0 1 1
## SB 1 0 0 0 2 0 3 0 2 0 0 0 1 11 3 8
## SD 3 0 1 0 1 0 2 1 3 5 1 0 3 6 20 1
## SSR 1 1 0 3 5 1 6 3 13 1 2 0 0 8 5 42
## TG 1 2 15 0 1 5 2 1 9 6 2 0 11 3 7 0
## WB 8 9 0 0 0 0 6 0 0 0 2 26 0 0 0 0
##
## altpreds TG WB
## AD 1 9
## Ant 2 0
## CBD 4 0
## CD 0 0
## CSR 2 0
## DC 9 0
## GLD 49 6
## IMS 8 0
## ISR 4 0
## LD 8 0
## LT 0 0
## MrD 0 4
## MxD 2 0
## SB 0 0
## SD 4 0
## SSR 3 0
## TG 104 0
## WB 0 81
## [1] "classification error rate with altdata: 0.598369011213048"
## [1] "Kappa overall = 0.35670424411517"
## [1] "Tau overall = 0.366432811656773"
## [1] "mean quality = 0.239785824898968"
## [1] "The quality is 0.239785824898968"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=5,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of roughnesscols is 0.559979581419091"
## MeanDecreaseGini parameters
## Texture 59.49776 Texture
## TRI_hr_ws47 23.38630 TRI_hr_ws47
## TRI_hr_ws46 21.31146 TRI_hr_ws46
## TRI_hr_ws44 21.22743 TRI_hr_ws44
## TRI_hr_ws42 20.23423 TRI_hr_ws42
## TRI_hr_ws45 19.87236 TRI_hr_ws45
## TRI_hr_ws43 19.57957 TRI_hr_ws43
## TRI_hr_ws40 17.99350 TRI_hr_ws40
## TRI_hr_ws41 16.94739 TRI_hr_ws41
## TRI_hr_ws38 16.75318 TRI_hr_ws38
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 38 5 0 2 0 0 1 0 0 1 2 19 0 2 2 0
## Ant 14 89 0 15 3 7 25 2 0 7 12 9 5 16 7 7
## CBD 3 3 95 2 19 0 2 9 21 14 0 0 21 12 39 13
## CD 4 1 0 24 0 13 10 1 0 3 11 3 5 0 0 3
## CSR 0 0 13 0 98 0 0 1 6 2 0 0 0 37 10 2
## DC 22 2 0 19 0 109 18 1 0 2 18 1 3 0 1 0
## GLD 25 32 1 61 2 23 245 40 2 20 66 7 41 26 5 12
## IMS 7 8 3 13 1 7 24 93 4 0 7 0 16 4 4 5
## ISR 1 0 27 5 10 0 10 7 84 6 2 0 32 11 27 32
## LD 6 11 2 12 0 8 9 0 2 101 14 0 3 8 7 5
## LT 6 6 0 28 0 12 19 4 0 3 39 3 10 8 0 4
## MrD 39 19 0 0 0 1 2 0 0 0 2 121 0 1 0 0
## MxD 3 3 5 3 0 11 12 12 10 1 5 0 32 5 6 6
## SB 5 6 1 3 6 0 10 5 5 4 4 0 1 9 9 6
## SD 3 1 16 1 9 0 2 3 29 15 2 0 4 22 43 18
## SSR 1 2 2 1 16 3 2 6 18 5 3 0 5 9 12 71
## TG 4 5 34 12 6 4 10 13 17 15 13 0 20 28 16 15
## WB 5 4 0 1 0 0 0 0 0 1 0 11 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 5 4
## CBD 21 0
## CD 11 0
## CSR 3 0
## DC 7 0
## GLD 54 7
## IMS 11 0
## ISR 18 0
## LD 14 3
## LT 21 2
## MrD 0 6
## MxD 8 0
## SB 4 0
## SD 6 0
## SSR 4 0
## TG 212 0
## WB 0 179
## [1] "classification error rate with altdata: 0.569270166453265"
## [1] "Prediction error at end is: 0.81243573765384"
## [2] "Prediction error at end is: 0.668703588305551"
## [3] "Prediction error at end is: 0.592257360959651"
## [4] "Prediction error at end is: 0.569311159578335"
## [5] "Prediction error at end is: 0.549935088539232"
## [6] "Prediction error at end is: 0.554516539440204"
## [7] "Prediction error at end is: 0.549415796853092"
## [8] "Prediction error at end is: 0.545340655346108"
## [9] "Prediction error at end is: 0.540751415069845"
## [10] "Prediction error at end is: 0.544822661889183"
## k 1 k 2
## 1 TRI_hr_ws24 TRI_hr_ws24
## 2 Texture Texture
## 3 vectorruggedness_hr_ws57 vectorruggedness_hr_ws55
## 4 vectorstrength_hr_ws59 vectorstrength_hr_ws57
## 5 vectorruggedness_hr_ws13 terraintexture_hr_ws45_tp25
## 6 terraintexture_hr_ws49_tp5 fischerk_ws11
## 7 terraintexture_hr_ws5_t1 vectorruggedness_hr_ws35
## 8 terraintexture_hr_ws37_tp25 terraintexture_hr_ws57_tp5
## 9 vectorruggedness_hr_ws27 terraintexture_hr_ws37_t1
## 10 terraintexture_hr_ws21_tp25 terraintexture_hr_ws17_t1
## k 3 k 4
## 1 TRI_hr_ws24 TRI_hr_ws24
## 2 Texture Texture
## 3 vectorruggedness_hr_ws57 vectorruggedness_hr_ws55
## 4 vectorstrength_hr_ws49 vectorstrength_hr_ws59
## 5 terraintexture_hr_ws29_tp25 terraintexture_hr_ws57_tp25
## 6 terraintexture_hr_ws57_tp5 terraintexture_hr_ws5_tp5
## 7 vectorstrength_hr_ws59 vectorruggedness_hr_ws23
## 8 vectorruggedness_hr_ws11 vectorstrength_hr_ws37
## 9 vectorstrength_hr_ws47 vectorruggedness_hr_ws57
## 10 vectorruggedness_hr_ws51 TRI_hr_ws22
## k 5
## 1 TRI_hr_ws24
## 2 Texture
## 3 vectorruggedness_hr_ws53
## 4 fischerk_ws59
## 5 terraintexture_hr_ws53_tp25
## 6 terraintexture_hr_ws33_tp5
## 7 terraintexture_hr_ws5_t1
## 8 vectorruggedness_hr_ws15
## 9 fischerk_ws25
## 10 terraintexture_hr_ws57_t1
## allchosen Freq
## 18 Texture 5
## 20 TRI_hr_ws24 5
## 30 vectorruggedness_hr_ws57 3
## 35 vectorstrength_hr_ws59 3
## 15 terraintexture_hr_ws57_tp5 2
## 16 terraintexture_hr_ws5_t1 2
## 29 vectorruggedness_hr_ws55 2predict_ranfor_newlegend_full_naproblem(modeldata=onehundred,dependent="geomorphologie_beschreibung",predictors=c("TRI_hr_ws24","Texture","vectorruggedness_hr_ws57","vectorstrength_hr_ws59","terraintexture_hr_ws53_tp25"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.534658511722732 for predictors TRI_hr_ws24 AND Texture AND vectorruggedness_hr_ws57 AND vectorstrength_hr_ws59 AND terraintexture_hr_ws53_tp25"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 38 3 0 4 0 2 1 2 1 2 3 19 1 1 0 0
## Ant 21 111 0 8 1 4 13 1 1 3 15 13 4 9 6 2
## CBD 0 0 95 1 19 0 0 6 21 11 0 0 24 14 34 5
## CD 7 5 2 47 0 15 22 2 0 5 14 1 12 5 3 1
## CSR 0 0 14 0 92 0 0 0 7 2 0 0 0 34 8 5
## DC 20 0 0 11 0 117 12 0 0 2 12 2 4 0 1 0
## GLD 13 25 4 54 4 16 248 44 5 13 55 10 28 27 11 9
## IMS 3 7 10 9 0 5 11 96 4 1 4 0 12 6 3 10
## ISR 2 0 26 5 9 0 11 6 72 3 2 0 24 11 23 23
## LD 7 8 3 8 4 8 13 2 2 119 13 0 4 5 5 7
## LT 7 5 0 28 1 12 25 7 0 5 38 1 15 4 0 3
## MrD 34 17 0 2 0 2 6 0 0 0 0 117 0 0 0 0
## MxD 6 6 7 10 1 4 8 8 19 2 11 0 38 4 16 10
## SB 5 1 4 3 16 0 9 8 3 4 9 2 5 30 10 10
## SD 5 1 11 2 7 1 5 4 26 14 4 0 7 20 51 22
## SSR 5 5 2 3 18 1 10 8 27 5 6 0 7 12 9 84
## TG 3 0 21 7 1 12 6 3 11 9 13 0 13 14 11 8
## WB 10 3 0 0 0 1 1 0 0 0 1 12 0 2 0 0
##
## altpreds TG WB
## AD 1 3
## Ant 10 2
## CBD 11 0
## CD 13 2
## CSR 0 0
## DC 14 0
## GLD 35 5
## IMS 6 0
## ISR 10 0
## LD 18 3
## LT 18 8
## MrD 0 8
## MxD 17 0
## SB 7 1
## SD 15 0
## SSR 8 0
## TG 216 0
## WB 1 169
## [1] "classification error rate with altdata: 0.546197039305768"
## [1] "Kappa overall = 0.417306507929602"
## [1] "Tau overall = 0.42167372308801"
## [1] "mean quality = 0.297114764397189"
## [1] "The quality is 0.297114764397189"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.469397029080755"RandomForest n=200
## [1] "Prediction error at end is: 0.816234003179816"
## [2] "Prediction error at end is: 0.664116949461777"
## [3] "Prediction error at end is: 0.586010438658222"
## [4] "Prediction error at end is: 0.554108022519353"
## [5] "Prediction error at end is: 0.520929444574765"
## [6] "Prediction error at end is: 0.514292230302082"
## [7] "Prediction error at end is: 0.512761943858003"
## [8] "Prediction error at end is: 0.519653608569865"
## [9] "Prediction error at end is: 0.519146336695598"
## [10] "Prediction error at end is: 0.511999244142101"
## k 1 k 2
## 1 Texture Texture
## 2 TRI_hr_ws26 TRI_hr_ws22
## 3 vectorruggedness_hr_ws59 vectorruggedness_hr_ws59
## 4 fischerk_ws61 fischerk_ws61
## 5 terraintexture_hr_ws57_tp25 terraintexture_hr_ws53_tp25
## 6 terraintexture_hr_ws57_tp5 vectorstrength_hr_ws31
## 7 vectorstrength_hr_ws23 terraintexture_hr_ws57_tp5
## 8 TRI_hr_ws6 terraintexture_hr_ws49_tp5
## 9 vectorruggedness_hr_ws35 terraintexture_hr_ws25_tp25
## 10 terraintexture_hr_ws9_t1 terraintexture_hr_ws41_t1
## k 3 k 4
## 1 Texture Texture
## 2 TRI_hr_ws26 TRI_hr_ws23
## 3 vectorruggedness_hr_ws57 vectorruggedness_hr_ws55
## 4 vectorstrength_hr_ws61 terraintexture_hr_ws57_tp25
## 5 terraintexture_hr_ws53_tp25 fischerk_ws61
## 6 vectorruggedness_hr_ws37 terraintexture_hr_ws57_tp5
## 7 terraintexture_hr_ws57_tp5 vectorruggedness_hr_ws37
## 8 terraintexture_hr_ws41_t1 TRI_hr_ws8
## 9 fischerk_ws39 terraintexture_hr_ws37_t1
## 10 terraintexture_hr_ws29_tp25 Melton_Ruggedness_Number_hr
## k 5
## 1 Texture
## 2 TRI_hr_ws25
## 3 vectorruggedness_hr_ws59
## 4 vectorstrength_hr_ws61
## 5 terraintexture_hr_ws57_tp25
## 6 fischerk_ws17
## 7 terraintexture_hr_ws49_t1
## 8 terraintexture_hr_ws57_tp5
## 9 terraintexture_hr_ws13_t1
## 10 terraintexture_hr_ws9_tp5
## allchosen Freq
## 14 terraintexture_hr_ws57_tp5 5
## 17 Texture 5
## 3 fischerk_ws61 3
## 13 terraintexture_hr_ws57_tp25 3
## 28 vectorruggedness_hr_ws59 3
## 9 terraintexture_hr_ws41_t1 2
## 12 terraintexture_hr_ws53_tp25 2
## 21 TRI_hr_ws26 2
## 25 vectorruggedness_hr_ws37 2
## 31 vectorstrength_hr_ws61 2predict_ranfor_newlegend_full_naproblem(modeldata=onehundred,dependent="geomorphologie_beschreibung",predictors=c("TRI_hr_ws26","Texture","vectorruggedness_hr_ws57","fischerk_ws61","terraintexture_hr_ws53_tp25"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.528542303771662 for predictors TRI_hr_ws26 AND Texture AND vectorruggedness_hr_ws57 AND fischerk_ws61 AND terraintexture_hr_ws53_tp25"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 40 1 0 7 0 1 0 2 1 2 4 20 1 3 0 0
## Ant 18 105 0 7 2 5 15 2 1 5 15 12 5 7 6 2
## CBD 1 0 94 0 18 0 1 7 24 9 0 0 20 13 32 6
## CD 7 8 2 49 0 16 20 1 0 4 9 1 10 4 3 1
## CSR 0 0 15 0 92 0 0 0 7 2 0 0 0 33 6 5
## DC 24 0 0 11 0 120 13 0 0 1 10 2 4 0 1 0
## GLD 12 27 4 50 3 12 240 41 6 15 55 10 30 26 9 8
## IMS 3 6 9 9 1 5 9 99 4 1 6 0 14 10 2 11
## ISR 1 0 23 4 8 1 14 5 76 1 2 0 29 11 25 25
## LD 4 7 4 6 4 8 15 2 1 121 14 0 4 4 6 7
## LT 7 8 0 28 1 17 27 8 0 5 40 1 14 4 0 1
## MrD 34 15 0 1 0 1 4 0 0 0 1 117 0 0 0 0
## MxD 6 6 8 11 0 4 11 10 14 2 10 0 34 5 20 9
## SB 5 2 6 3 16 0 10 4 6 4 10 3 4 30 11 9
## SD 6 2 10 2 9 1 2 3 23 15 4 0 6 20 50 23
## SSR 4 6 3 4 18 1 12 9 25 4 2 0 8 15 12 83
## TG 4 0 21 8 1 8 6 4 11 9 17 0 15 11 8 9
## WB 10 4 0 2 0 0 2 0 0 0 1 11 0 2 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 8 3
## CBD 11 0
## CD 10 1
## CSR 0 0
## DC 14 0
## GLD 36 5
## IMS 7 0
## ISR 8 0
## LD 17 3
## LT 18 7
## MrD 0 14
## MxD 17 0
## SB 8 1
## SD 16 0
## SSR 12 0
## TG 216 1
## WB 1 166
## [1] "classification error rate with altdata: 0.547728432873915"
## [1] "Kappa overall = 0.415804167759251"
## [1] "Tau overall = 0.420052247545266"
## [1] "mean quality = 0.296344263886633"
## [1] "The quality is 0.296344263886633"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.468545035166489"RLI
SVM n=100
## [1] "Prediction error at end is: 0.889981565145142"
## [2] "Prediction error at end is: 0.831887105987433"
## [3] "Prediction error at end is: 0.80692994755154"
## [4] "Prediction error at end is: 0.784513423690087"
## [5] "Prediction error at end is: 0.764649218466012"
## [6] "Prediction error at end is: 0.758525471257205"
## [7] "Prediction error at end is: 0.75700524484603"
## [8] "Prediction error at end is: 0.754459417354728"
## [9] "Prediction error at end is: 0.746818040193177"
## [10] "Prediction error at end is: 0.747828062522719"
## k 1
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_simpson_UE_hr_10cells
## 3 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 4 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## k 2
## 1 geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 6 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 8 geom_hr_L3_fl10_r_li_simpson_UE_hr_10cells
## 9 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## k 3
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 5 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 6 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 7 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 8 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_richness_UE_hr_10cells
## k 4
## 1 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 2 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 5 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 6 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_patchnum_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_10cells
## 9 geom_hr_L3_fl1_r_li_mps_UE_hr_10cells
## 10 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## k 5
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 3 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 6 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 8 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 9 geom_hr_L50m_fl1_r_li_dominance_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## allchosen Freq
## 1 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells 5
## 6 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells 5
## 3 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells 4
## 16 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells 4
## 9 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells 3
## 12 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells 3
## 11 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells 2
## 13 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells 2
## 17 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells 2
## 20 geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells 2
## 21 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells 2
## 23 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells 2
## 24 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells 2## [1] "10fold cv-error: 0.769740193581253 for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells AND geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ant 1 3 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0
## CBD 0 0 3 0 0 4 1 0 5 1 1 0 0 3 1 2 2
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 5 2 20 3 53 7 11 2 12 8 7 0 4 15 24 3 6
## DC 3 2 2 13 3 21 7 3 2 6 8 0 1 1 2 1 4
## GLD 18 19 7 38 2 16 85 31 2 12 41 4 27 25 6 12 32
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 2 2 27 9 8 13 11 8 38 14 3 1 14 11 21 25 23
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 1 1 0 2 1 5 2 0 0 0 8 0 4 1 0 0 3
## MrD 31 20 0 4 0 2 19 0 0 0 1 67 0 0 0 0 0
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SD 1 1 8 0 7 2 2 1 5 1 3 0 1 6 12 10 1
## SSR 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TG 18 44 34 30 16 20 58 52 36 58 23 1 48 38 34 47 127
## WB 10 4 0 2 0 10 4 0 0 0 5 10 1 0 0 0 2
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 0
## CSR 0
## DC 3
## GLD 9
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 7
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 81
## [1] "Kappa overall = 0.19085277704154"
## [1] "Tau overall = 0.209792933984597"
## [1] "mean quality = 0.110356921968066"
## [1] "The quality is 0.110356921968066"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ant 0 1 0 1 0 0 0 0 0 2 0 0 0 0 0 0
## CBD 5 0 4 2 3 6 4 1 2 3 2 0 5 4 10 6
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 7 4 31 10 61 8 25 7 23 22 5 0 8 34 37 13
## DC 14 12 4 31 4 19 20 8 5 11 19 3 5 3 8 4
## GLD 27 30 14 59 11 45 147 62 5 44 59 13 56 32 16 31
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 6 3 50 11 39 22 23 28 66 13 12 0 37 27 50 44
## LD 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0
## LT 1 6 0 6 1 13 9 0 2 1 9 0 1 0 1 1
## MrD 55 37 0 10 0 8 42 0 0 1 11 137 4 3 0 0
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SD 1 0 16 2 19 5 7 0 10 7 2 0 6 20 17 14
## SSR 0 0 2 0 0 0 1 1 1 0 0 0 0 0 1 1
## TG 41 100 78 67 35 57 112 90 85 95 72 2 76 75 51 85
## WB 29 3 0 3 0 17 10 0 0 0 8 23 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 2 1
## CBD 4 0
## CD 0 0
## CSR 17 0
## DC 10 3
## GLD 72 10
## IMS 0 0
## ISR 36 0
## LD 0 0
## LT 5 2
## MrD 2 6
## MxD 0 0
## SB 0 0
## SD 3 0
## SSR 0 0
## TG 249 10
## WB 0 169
## [1] "classification error rate with altdata: 0.775197754529217"
## [1] "Kappa overall = 0.159676428839987"
## [1] "Tau overall = 0.1792023775573"
## [1] "mean quality = 0.0943794212714953"
## [1] "The quality is 0.0943794212714953"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"SVM n=200
## [1] "Prediction error at end is: 0.880581945421847"
## [2] "Prediction error at end is: 0.821640667761357"
## [3] "Prediction error at end is: 0.791527223916387"
## [4] "Prediction error at end is: 0.77111091563062"
## [5] "Prediction error at end is: 0.757076067975083"
## [6] "Prediction error at end is: 0.755287095681184"
## [7] "Prediction error at end is: 0.748912476868142"
## [8] "Prediction error at end is: 0.741258764042015"
## [9] "Prediction error at end is: 0.735134686058331"
## [10] "Prediction error at end is: 0.731306200641176"
## k 1
## 1 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 7 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 8 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## k 2
## 1 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 5 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_simpson_UE_hr_10cells
## 9 geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## k 3
## 1 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 2 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 6 geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 7 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 8 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## k 4
## 1 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 2 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 4 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 6 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 7 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 8 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 9 geom_hr_L3_fl10_r_li_simpson_UE_hr_10cells
## 10 geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## k 5
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 6 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 7 geom_hr_L3_fl10_r_li_mps_UE_hr_10cells
## 8 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## allchosen Freq
## 1 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells 5
## 14 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells 5
## 19 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells 5
## 4 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells 4
## 10 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells 4
## 20 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells 4
## 12 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells 3
## 3 geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells 2
## 5 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells 2
## 8 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells 2
## 11 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells 2
## 13 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells 2
## 17 geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells 2## [1] "10fold cv-error: 0.777749425873947 for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells AND geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 5 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0
## Ant 1 4 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## CBD 0 0 21 0 10 5 4 2 6 6 3 0 6 4 5 8 1
## CD 0 3 0 9 0 5 3 0 1 1 4 1 1 0 0 0 3
## CSR 7 2 22 12 50 6 16 6 16 17 7 0 7 28 24 5 15
## DC 3 4 1 2 2 4 4 0 1 0 0 1 1 1 3 1 2
## GLD 50 57 18 102 22 76 214 81 14 54 95 28 72 39 20 36 93
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 3 2 28 13 23 24 19 30 62 7 7 0 35 24 29 32 35
## LD 0 2 1 2 1 2 2 1 0 6 7 0 1 3 0 1 4
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 50 30 0 3 0 6 18 0 0 0 6 125 1 3 0 0 1
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SD 8 1 36 8 36 13 20 4 26 16 5 0 10 32 54 31 10
## SSR 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## TG 39 90 72 48 29 42 90 73 73 92 59 0 64 64 56 85 236
## WB 20 2 0 3 0 16 9 0 0 0 7 22 0 0 0 0 0
##
## preds WB
## AD 3
## Ant 0
## CBD 0
## CD 1
## CSR 0
## DC 2
## GLD 14
## IMS 0
## ISR 1
## LD 1
## LT 0
## MrD 5
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 11
## WB 163
## [1] "Kappa overall = 0.177203088186952"
## [1] "Tau overall = 0.198655119103012"
## [1] "mean quality = 0.107043093789178"
## [1] "The quality is 0.107043093789178"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 2 1 0 0 0 0 1 0 0 0 1 6 1 0 0 0
## Ant 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## CBD 1 0 1 1 1 0 3 0 1 1 1 0 2 1 6 6
## CD 0 2 0 4 0 4 1 0 1 2 0 0 0 0 0 0
## CSR 3 2 11 5 41 6 9 1 12 6 4 0 3 11 15 0
## DC 0 1 1 1 0 1 3 0 2 2 1 0 0 0 0 0
## GLD 26 22 10 47 8 40 107 40 3 21 63 14 34 31 8 15
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 1 3 19 8 9 14 7 8 33 7 2 1 14 6 12 26
## LD 0 1 0 2 0 0 0 1 0 3 1 0 2 0 1 0
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 31 17 0 2 0 2 13 0 0 0 0 58 0 0 0 0
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SD 3 1 23 2 18 8 7 2 14 8 5 0 2 14 24 17
## SSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TG 17 47 36 26 14 15 46 45 34 50 18 2 42 37 34 36
## WB 6 2 0 2 0 10 3 0 0 0 4 3 0 0 0 0
##
## altpreds TG WB
## AD 1 2
## Ant 0 0
## CBD 3 0
## CD 0 0
## CSR 3 0
## DC 1 1
## GLD 54 12
## IMS 0 0
## ISR 20 0
## LD 1 0
## LT 0 0
## MrD 0 8
## MxD 0 0
## SB 0 0
## SD 7 0
## SSR 0 0
## TG 109 0
## WB 1 77
## [1] "classification error rate with altdata: 0.765664798777382"
## [1] "Kappa overall = 0.167535513331501"
## [1] "Tau overall = 0.189296095412184"
## [1] "mean quality = 0.101971529130827"
## [1] "The quality is 0.101971529130827"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=4,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of rlicols is 0.568577277379734"
## MeanDecreaseGini
## geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells 22.29497
## geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells 20.14078
## geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells 20.12305
## geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells 20.10577
## geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells 18.92739
## geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells 18.87843
## geom_hr_L3_fl1_r_li_shape_UE_hr_60cells 18.85994
## geom_hr_L3_fl1_r_li_mps_UE_hr_60cells 18.82247
## geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells 18.73360
## geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells 18.11850
## parameters
## geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells
## geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells
## geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells
## geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells
## geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells
## geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells
## geom_hr_L3_fl1_r_li_shape_UE_hr_60cells geom_hr_L3_fl1_r_li_shape_UE_hr_60cells
## geom_hr_L3_fl1_r_li_mps_UE_hr_60cells geom_hr_L3_fl1_r_li_mps_UE_hr_60cells
## geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells
## geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 29 5 0 0 0 0 1 0 0 0 0 15 1 0 0 0
## Ant 19 99 0 6 1 2 16 1 4 9 3 11 4 10 6 0
## CBD 2 0 68 2 19 0 0 1 15 12 4 0 4 11 24 11
## CD 4 7 0 26 0 12 12 1 0 0 6 3 4 2 2 0
## CSR 2 0 25 0 79 0 2 5 27 10 0 0 10 36 36 10
## DC 17 2 1 36 0 78 14 1 0 4 24 0 1 3 0 0
## GLD 29 26 1 85 5 28 256 48 2 15 80 5 42 29 13 13
## IMS 4 4 1 4 1 2 11 85 0 0 3 1 5 7 1 3
## ISR 0 0 25 4 27 0 9 4 86 2 5 0 36 11 26 45
## LD 6 10 2 5 1 7 5 1 0 99 7 0 1 9 7 7
## LT 7 3 0 6 2 28 16 0 0 0 23 1 8 3 4 0
## MrD 41 17 0 1 0 4 9 0 0 0 4 128 0 0 0 0
## MxD 3 0 5 6 1 9 11 11 8 0 8 0 27 4 6 3
## SB 4 0 2 3 5 0 3 2 3 2 3 0 3 9 6 5
## SD 0 0 22 1 11 0 2 0 11 14 0 0 2 11 29 9
## SSR 0 2 15 3 7 1 9 2 17 2 2 0 10 7 7 43
## TG 8 21 30 14 13 25 23 35 24 31 28 1 39 46 24 47
## WB 11 1 0 0 0 3 2 0 0 0 0 12 0 0 0 0
##
## altpreds TG WB
## AD 0 1
## Ant 4 6
## CBD 8 0
## CD 10 2
## CSR 1 0
## DC 16 1
## GLD 51 9
## IMS 6 0
## ISR 23 0
## LD 5 0
## LT 5 0
## MrD 0 2
## MxD 9 0
## SB 1 0
## SD 4 0
## SSR 6 0
## TG 250 1
## WB 0 176
## [1] "classification error rate with altdata: 0.592725409836066"
## [1] "Prediction error at end is: 0.872634626369632"
## [2] "Prediction error at end is: 0.835455938100431"
## [3] "Prediction error at end is: 0.819146284467986"
## [4] "Prediction error at end is: 0.807437555174742"
## [5] "Prediction error at end is: 0.775849041906839"
## [6] "Prediction error at end is: 0.743769797995534"
## [7] "Prediction error at end is: 0.752921015734538"
## [8] "Prediction error at end is: 0.739685568884042"
## [9] "Prediction error at end is: 0.733059406968894"
## [10] "Prediction error at end is: 0.733568312821312"
## k 1
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_shape_UE_hr_20cells
## 3 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 4 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 5 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 7 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 8 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9 geom_hr_L3_fl10_r_li_edgedensity_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## k 2
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 3 geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 5 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6 geom_hr_L3_fl10_r_li_mps_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_mps_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 9 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 10 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## k 3
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_10cells
## 2 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_20cells
## 4 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 5 geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 7 geom_hr_L3_fl10_r_li_shape_UE_hr_20cells
## 8 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 9 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## k 4
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3 geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_shape_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 8 geom_hr_L3_fl1_r_li_simpson_UE_hr_5cells
## 9 geom_hr_L50m_fl1_r_li_richness_UE_hr_10cells
## 10 geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
## k 5
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_10cells
## 2 geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 5 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6 geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 7 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells
## 9 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_10cells
## allchosen Freq
## 2 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells 5
## 13 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells 4
## 6 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells 3
## 7 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells 3
## 11 geom_hr_L3_fl1_r_li_mps_UE_hr_20cells 3
## 5 geom_hr_L3_fl10_r_li_richness_UE_hr_10cells 2
## 8 geom_hr_L3_fl10_r_li_shape_UE_hr_20cells 2
## 10 geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells 2
## 14 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells 2
## 18 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells 2
## 20 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells 2
## 21 geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells 2
## 27 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells 2predict_ranfor_newlegend_full_naproblem(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_hr_L3_fl10_r_li_richness_UE_hr_20cells","geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.79786150712831 for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"
## [1] "Kappa overall = 0.285943616058494"
## [1] "Tau overall = 0.299149394992213"
## [1] "mean quality = 0.179411764982154"
## [1] "The quality is 0.179411764982154"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.362649457558158"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 2 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0
## Ant 2 7 6 6 1 6 16 10 4 13 2 0 5 6 3 5
## CBD 1 2 3 1 3 2 5 1 0 0 1 0 1 2 1 0
## CD 2 5 2 5 0 6 13 8 1 8 10 0 7 4 0 7
## CSR 4 5 50 6 75 2 27 7 39 24 6 9 14 54 61 27
## DC 19 14 7 34 10 37 42 16 10 16 29 4 22 12 13 13
## GLD 31 30 23 50 13 44 91 45 16 45 44 0 27 28 20 40
## IMS 2 6 4 1 2 4 5 7 5 4 6 1 8 1 3 5
## ISR 5 12 11 11 20 7 14 11 20 11 15 9 14 11 11 9
## LD 0 5 6 1 1 1 5 6 1 3 0 1 3 1 1 8
## LT 2 0 0 2 0 6 11 5 0 2 2 2 1 3 1 0
## MrD 51 31 2 17 1 24 35 0 3 1 22 104 3 3 2 3
## MxD 2 3 2 5 2 2 6 7 2 4 1 1 3 5 2 1
## SB 4 0 5 0 7 0 2 0 1 1 0 0 0 1 6 1
## SD 0 3 2 0 6 0 2 0 2 1 1 2 1 2 1 2
## SSR 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 2
## TG 27 59 76 52 34 31 103 73 95 65 49 6 85 65 66 76
## WB 32 14 0 10 0 28 22 0 0 0 12 42 3 0 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 6 0
## CBD 3 0
## CD 10 2
## CSR 18 2
## DC 28 2
## GLD 77 6
## IMS 14 0
## ISR 23 3
## LD 2 0
## LT 3 1
## MrD 5 22
## MxD 5 0
## SB 0 0
## SD 4 0
## SSR 3 0
## TG 191 5
## WB 7 158
## [1] "classification error rate with altdata: 0.81855249745158"
## [1] "Kappa overall = 0.116912653239332"
## [1] "Tau overall = 0.133297355639504"
## [1] "mean quality = 0.0738530903316026"
## [1] "The quality is 0.0738530903316026"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.228346300619059"RandomForest n=200
## [1] "Prediction error at end is: 0.88569082805536"
## [2] "Prediction error at end is: 0.834908254489535"
## [3] "Prediction error at end is: 0.81424140537441"
## [4] "Prediction error at end is: 0.802244767638856"
## [5] "Prediction error at end is: 0.781320861678005"
## [6] "Prediction error at end is: 0.764991073057576"
## [7] "Prediction error at end is: 0.748661936038783"
## [8] "Prediction error at end is: 0.745091810670628"
## [9] "Prediction error at end is: 0.734885448432246"
## [10] "Prediction error at end is: 0.728506268407746"
## k 1
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 3 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 4 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 5 geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 6 geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 7 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 8 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 10 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## k 2
## 1 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 5 geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 7 geom_hr_L3_fl1_r_li_shape_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 9 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 10 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## k 3
## 1 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 2 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 5 geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_edgedensity_UE_hr_20cells
## 7 geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 8 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9 geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
## 10 geom_hr_L3_fl10_r_li_shape_UE_hr_5cells
## k 4
## 1 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_mps_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4 geom_hr_L50m_fl1_r_li_dominance_UE_hr_20cells
## 5 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 6 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 7 geom_hr_L3_fl10_r_li_mps_UE_hr_20cells
## 8 geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_richness_UE_hr_10cells
## 10 geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## k 5
## 1 geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
## 2 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 4 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5 geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells
## 6 geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 8 geom_hr_L50m_fl1_r_li_shape_UE_hr_20cells
## 9 geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## allchosen Freq
## 1 geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells 5
## 5 geom_hr_L3_fl10_r_li_richness_UE_hr_20cells 4
## 16 geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells 3
## 6 geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells 2
## 9 geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells 2
## 13 geom_hr_L3_fl1_r_li_richness_UE_hr_20cells 2
## 18 geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells 2
## 19 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells 2
## 20 geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells 2
## 21 geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells 2
## 26 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells 2
## 27 geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells 2
## 28 geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells 2
## 30 geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells 2predict_ranfor_newlegend_full_naproblem(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("geom_hr_L3_fl10_r_li_richness_UE_hr_20cells","geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.809123343527013 for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"
## [1] "Kappa overall = 0.201828696032813"
## [1] "Tau overall = 0.220723151645979"
## [1] "mean quality = 0.116666594309716"
## [1] "The quality is 0.116666594309716"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.289069713308378"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## Ant 0 1 0 0 0 0 2 2 0 3 1 0 1 2 0 1
## CBD 0 0 2 0 0 0 2 2 0 0 1 0 0 0 0 0
## CD 0 0 0 0 0 3 1 0 1 0 1 0 1 0 0 0
## CSR 8 11 37 9 68 9 23 5 33 22 8 6 11 30 43 18
## DC 5 1 0 6 0 7 11 3 0 1 6 0 2 1 1 3
## GLD 10 20 10 30 5 34 46 32 6 16 28 3 16 15 12 13
## IMS 0 0 0 1 0 0 5 3 0 1 3 0 0 1 0 0
## ISR 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0
## LD 2 2 3 1 0 0 4 2 1 2 0 0 2 3 0 0
## LT 3 2 2 7 0 6 6 2 2 2 3 0 1 1 0 2
## MrD 31 15 0 8 1 11 15 0 0 0 8 52 2 3 0 0
## MxD 0 1 0 0 0 0 0 0 3 0 1 0 0 1 0 2
## SB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SSR 1 0 0 0 0 0 1 0 1 2 0 0 0 0 1 1
## TG 15 39 47 33 17 17 74 44 53 50 25 6 61 42 43 60
## WB 14 7 0 5 0 13 10 0 0 0 15 17 3 1 0 0
##
## altpreds TG WB
## AD 0 2
## Ant 4 0
## CBD 1 0
## CD 0 0
## CSR 14 1
## DC 5 2
## GLD 28 0
## IMS 2 0
## ISR 1 0
## LD 1 0
## LT 4 0
## MrD 4 11
## MxD 0 0
## SB 0 0
## SD 0 0
## SSR 1 0
## TG 133 2
## WB 3 82
## [1] "classification error rate with altdata: 0.795824847250509"
## [1] "Kappa overall = 0.13711098087002"
## [1] "Tau overall = 0.157361926440637"
## [1] "mean quality = 0.0723346307152706"
## [1] "The quality is 0.0723346307152706"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"HEIGHTS
SVM n=100
## [1] "Prediction error at end is: 0.738422391857506"
## [2] "Prediction error at end is: 0.638167938931298"
## [3] "Prediction error at end is: 0.590330788804071"
## [4] "Prediction error at end is: 0.583715012722646"
## [5] "Prediction error at end is: 0.557251908396947"
## [6] "Prediction error at end is: 0.542493638676845"
## [7] "Prediction error at end is: 0.540458015267176"
## [8] "Prediction error at end is: 0.536895674300254"
## [9] "Prediction error at end is: 0.530788804071247"
## [10] "Prediction error at end is: 0.525190839694657"
## k 1
## 1 Maximum_Height_hr
## 2 ValleyDepth
## 3 Vertical_Distance_to_Channel_Network
## 4 SlopeHeight
## 5 Valley_Depth_hr
## 6 Standardized_Height
## 7 Mid_Slope_Positon
## 8 Slope_Height
## 9 NormalizedHeight
## 10 Standardized_Height_hr
## k 2
## 1 Maximum_Height_hr
## 2 ValleyDepth
## 3 Vertical_Distance_to_Channel_Network
## 4 NormalizedHeight
## 5 SlopeHeight
## 6 Valley_Depth_hr
## 7 Normalized_Height
## 8 Slope_Height
## 9 Standardized_Height
## 10 Mid_Slope_Positon
## k 3
## 1 Maximum_Height_hr
## 2 ValleyDepth
## 3 Vertical_Distance_to_Channel_Network
## 4 SlopeHeight
## 5 Valley_Depth_hr
## 6 Normalized_Height
## 7 NormalizedHeight
## 8 Slope_Height
## 9 Slope_Height_hr
## 10 Mid_Slope_Positon
## k 4
## 1 Maximum_Height_hr
## 2 ValleyDepth
## 3 Vertical_Distance_to_Channel_Network
## 4 SlopeHeight
## 5 Valley_Depth_hr
## 6 Standardized_Height
## 7 Normalized_Height
## 8 Slope_Height
## 9 Standardized_Height_hr
## 10 Mid_Slope_Positon
## k 5
## 1 Maximum_Height_hr
## 2 ValleyDepth
## 3 Vertical_Distance_to_Channel_Network
## 4 NormalizedHeight
## 5 Slope_Height
## 6 Standardized_Height
## 7 Valley_Depth_hr
## 8 Mid_Slope_Positon
## 9 Slope_Height_hr
## 10 SlopeHeight
## allchosen Freq
## 1 Maximum_Height_hr 5
## 2 Mid_Slope_Positon 5
## 5 SlopeHeight 5
## 6 Slope_Height 5
## 10 ValleyDepth 5
## 11 Valley_Depth_hr 5
## 12 Vertical_Distance_to_Channel_Network 5
## 3 NormalizedHeight 4
## 8 Standardized_Height 4
## 4 Normalized_Height 3
## 7 Slope_Height_hr 2
## 9 Standardized_Height_hr 2## [1] "10fold cv-error: 0.526208651399491 for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 36 18 0 5 0 12 3 3 0 0 1 28 0 0 0 0 0
## Ant 0 18 0 0 0 0 3 0 0 0 3 0 0 3 0 0 0
## CBD 0 0 72 0 10 0 0 2 3 2 1 0 1 2 13 0 9
## CD 1 0 0 11 0 2 1 1 1 4 1 0 9 5 2 0 4
## CSR 0 0 2 0 75 0 0 0 0 0 0 0 1 0 7 0 0
## DC 3 6 3 4 0 22 1 0 1 5 1 0 6 1 2 0 0
## GLD 27 15 1 43 0 34 177 34 0 23 32 0 9 1 3 0 26
## IMS 0 1 0 2 0 0 4 18 0 0 0 0 0 1 0 0 0
## ISR 0 0 11 3 2 0 0 0 63 0 1 0 6 1 16 6 12
## LD 0 0 0 0 0 0 0 4 0 41 3 0 7 4 5 0 10
## LT 3 1 0 5 0 0 3 8 0 9 47 1 0 5 4 0 7
## MrD 21 26 0 5 0 14 1 2 0 0 0 53 0 0 0 0 0
## MxD 0 5 0 6 0 4 0 0 3 0 1 0 27 2 2 2 2
## SB 0 0 0 0 0 0 0 2 0 1 4 0 0 61 9 2 7
## SD 0 0 2 0 2 0 0 0 2 1 1 1 3 4 22 0 3
## SSR 0 0 0 7 2 1 0 0 19 1 0 0 7 4 5 86 7
## TG 0 7 9 5 0 8 0 12 8 13 4 0 22 4 7 4 113
## WB 0 2 1 5 0 3 7 11 0 0 0 1 2 2 3 0 1
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 1
## CSR 0
## DC 0
## GLD 1
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 0
## MxD 0
## SB 1
## SD 0
## SSR 0
## TG 0
## WB 97
## [1] "Kappa overall = 0.495109085172924"
## [1] "Tau overall = 0.501032779524023"
## [1] "mean quality = 0.35223497027564"
## [1] "The quality is 0.35223497027564"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.546987394231187"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 72 25 0 13 0 17 18 7 0 0 1 63 1 0 2 0
## Ant 2 28 0 0 0 0 16 5 0 0 7 0 0 0 0 0
## CBD 0 0 110 0 20 0 0 5 9 3 0 0 9 2 35 0
## CD 0 0 4 14 0 14 1 2 1 5 0 0 14 3 2 0
## CSR 0 0 11 0 123 0 0 0 1 0 0 0 1 0 12 1
## DC 1 9 7 7 0 36 1 2 1 9 1 1 14 2 4 0
## GLD 46 41 2 86 0 73 336 84 0 33 52 0 23 10 5 0
## IMS 2 1 1 0 0 0 4 30 0 0 2 1 4 6 0 0
## ISR 0 0 21 7 23 1 0 0 104 3 0 0 28 0 39 16
## LD 0 1 0 4 0 0 0 7 0 83 11 0 2 5 6 0
## LT 4 10 0 14 0 0 3 16 0 23 99 3 0 21 6 0
## MrD 48 52 0 4 0 21 4 6 0 0 0 111 1 0 0 0
## MxD 0 7 3 10 0 11 0 0 18 1 2 0 46 3 5 16
## SB 1 0 8 0 0 1 2 1 6 4 8 0 0 108 16 1
## SD 0 0 3 1 6 1 1 0 6 2 3 0 2 5 32 1
## SSR 0 0 5 13 2 4 0 0 40 0 1 0 16 9 13 158
## TG 0 18 24 20 1 21 0 21 13 34 12 0 37 22 10 6
## WB 10 5 0 9 0 1 15 11 0 0 1 3 0 2 6 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 10 0
## CD 4 5
## CSR 0 0
## DC 2 0
## GLD 54 6
## IMS 3 0
## ISR 28 0
## LD 14 0
## LT 20 2
## MrD 0 0
## MxD 13 0
## SB 12 0
## SD 11 1
## SSR 13 0
## TG 214 0
## WB 2 187
## [1] "classification error rate with altdata: 0.518584521384929"
## [1] "Kappa overall = 0.444154688984576"
## [1] "Tau overall = 0.450910506768899"
## [1] "mean quality = 0.308337012539322"
## [1] "The quality is 0.308337012539322"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.503198571947649"SVM n=200
## [1] "Prediction error at end is: 0.6967893551158"
## [2] "Prediction error at end is: 0.624486475097648"
## [3] "Prediction error at end is: 0.57814816615614"
## [4] "Prediction error at end is: 0.550914571887003"
## [5] "Prediction error at end is: 0.511965122121197"
## [6] "Prediction error at end is: 0.488030339864832"
## [7] "Prediction error at end is: 0.491592032544043"
## [8] "Prediction error at end is: 0.489044261843406"
## [9] "Prediction error at end is: 0.483699453817604"
## [10] "Prediction error at end is: 0.483446621610671"
## k 1
## 1 Maximum_Height_hr
## 2 Normalized_Height
## 3 ValleyDepth
## 4 Vertical_Distance_to_Channel_Network
## 5 Valley_Depth_hr
## 6 Slope_Height
## 7 SlopeHeight
## 8 Mid_Slope_Positon
## 9 Relative_Slope_Position
## 10 Standardized_Height_hr
## k 2
## 1 Maximum_Height_hr
## 2 Normalized_Height
## 3 ValleyDepth
## 4 Vertical_Distance_to_Channel_Network
## 5 Valley_Depth_hr
## 6 Slope_Height
## 7 SlopeHeight
## 8 Standardized_Height
## 9 Mid_Slope_Positon
## 10 StandardizedHeight
## k 3
## 1 Maximum_Height_hr
## 2 Normalized_Height
## 3 Slope_Height
## 4 Vertical_Distance_to_Channel_Network
## 5 ValleyDepth
## 6 Valley_Depth_hr
## 7 StandardizedHeight
## 8 SlopeHeight
## 9 Relative_Slope_Position
## 10 Mid_Slope_Positon
## k 4
## 1 Maximum_Height_hr
## 2 Normalized_Height
## 3 ValleyDepth
## 4 Slope_Height
## 5 Vertical_Distance_to_Channel_Network
## 6 Valley_Depth_hr
## 7 SlopeHeight
## 8 Relative_Slope_Position
## 9 NormalizedHeight
## 10 StandardizedHeight
## k 5
## 1 Maximum_Height_hr
## 2 Normalized_Height
## 3 ValleyDepth
## 4 Valley_Depth_hr
## 5 Vertical_Distance_to_Channel_Network
## 6 Slope_Height
## 7 SlopeHeight
## 8 Relative_Slope_Position
## 9 StandardizedHeight
## 10 Standardized_Height
## allchosen Freq
## 1 Maximum_Height_hr 5
## 4 Normalized_Height 5
## 6 SlopeHeight 5
## 7 Slope_Height 5
## 11 ValleyDepth 5
## 12 Valley_Depth_hr 5
## 13 Vertical_Distance_to_Channel_Network 5
## 5 Relative_Slope_Position 4
## 8 StandardizedHeight 4
## 2 Mid_Slope_Positon 3
## 9 Standardized_Height 2## [1] "10fold cv-error: 0.484725050916497 for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 40 0 0 5 0 0 6 8 0 0 0 7 0 1 0 0 1
## Ant 1 38 0 0 0 0 9 6 0 0 6 1 0 1 1 0 1
## CBD 0 0 127 0 22 0 0 4 7 1 0 0 9 1 24 1 9
## CD 0 5 3 26 0 12 2 1 1 5 1 0 18 1 1 0 4
## CSR 0 0 1 0 133 1 0 0 2 0 0 0 0 0 14 1 0
## DC 7 5 5 25 0 96 8 11 2 20 2 0 18 3 2 0 21
## GLD 40 44 3 73 0 38 337 65 0 18 32 0 19 10 8 0 31
## IMS 4 3 1 2 0 0 8 46 0 0 3 0 3 4 0 0 1
## ISR 0 0 20 3 13 1 0 0 127 2 0 0 23 4 32 18 31
## LD 0 1 4 4 0 0 0 10 1 111 14 0 3 8 6 0 17
## LT 8 3 0 13 0 0 3 16 0 15 123 3 0 19 4 0 22
## MrD 83 73 0 10 0 36 11 5 0 0 0 167 1 0 0 0 0
## MxD 0 8 2 12 0 7 0 0 11 0 1 0 53 2 2 5 7
## SB 0 0 4 0 0 1 2 2 2 4 5 0 0 116 13 0 14
## SD 0 0 4 0 5 1 1 0 10 3 1 1 4 3 61 1 10
## SSR 0 0 5 15 2 2 0 0 30 0 1 0 15 7 11 163 11
## TG 0 12 20 6 0 5 0 13 6 21 9 0 32 17 8 10 218
## WB 3 5 0 8 0 1 14 10 0 0 2 3 0 1 6 0 2
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 1
## CSR 0
## DC 0
## GLD 5
## IMS 0
## ISR 0
## LD 0
## LT 2
## MrD 0
## MxD 0
## SB 0
## SD 1
## SSR 0
## TG 1
## WB 191
## [1] "Kappa overall = 0.522474583639682"
## [1] "Tau overall = 0.526925841619744"
## [1] "mean quality = 0.374567214521099"
## [1] "The quality is 0.374567214521099"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.56356126374158"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 7 0 0 1 0 1 1 4 0 0 1 3 0 0 0 0
## Ant 0 19 0 0 0 1 3 0 0 1 2 0 0 3 1 0
## CBD 0 0 62 0 10 0 0 4 6 1 0 0 1 4 14 2
## CD 0 3 1 13 0 2 1 0 1 4 2 0 10 2 1 0
## CSR 0 0 3 0 68 0 0 0 0 0 0 0 2 0 5 0
## DC 6 5 3 17 0 37 9 5 1 12 1 0 10 2 1 0
## GLD 24 18 2 31 0 22 165 28 0 10 24 0 7 1 4 0
## IMS 1 1 0 2 0 1 7 25 0 0 4 0 3 1 0 0
## ISR 0 0 14 3 3 1 0 0 59 0 0 0 12 0 17 9
## LD 0 0 2 1 0 0 0 3 0 55 4 0 6 5 4 0
## LT 3 0 0 5 0 0 5 8 0 6 53 1 0 8 4 0
## MrD 50 42 0 7 0 24 2 3 0 0 0 78 0 0 0 0
## MxD 0 4 0 5 1 2 0 0 4 0 0 0 17 4 3 3
## SB 0 0 1 0 0 0 0 3 0 0 3 0 0 56 9 1
## SD 0 0 5 0 8 0 0 0 10 3 3 1 3 3 23 0
## SSR 0 0 0 7 1 1 0 0 17 1 0 0 8 2 4 79
## TG 0 5 7 4 0 6 0 5 2 7 3 0 19 7 7 6
## WB 0 2 1 5 0 2 7 9 0 0 0 1 2 2 3 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 11 0
## CD 1 1
## CSR 1 0
## DC 11 0
## GLD 12 1
## IMS 1 0
## ISR 12 0
## LD 11 0
## LT 7 0
## MrD 0 0
## MxD 6 0
## SB 9 1
## SD 3 0
## SSR 6 0
## TG 108 0
## WB 2 97
## [1] "classification error rate with altdata: 0.480407124681934"
## [1] "Kappa overall = 0.486902453212378"
## [1] "Tau overall = 0.491333632689717"
## [1] "mean quality = 0.339178536857587"
## [1] "The quality is 0.339178536857587"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.536301366540095"RandomForest n=100
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=6,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of heights is 0.313994910941476"
## MeanDecreaseGini
## Maximum_Height_hr 262.57103
## Vertical_Distance_to_Channel_Network 160.76117
## Relative_Slope_Position 157.89584
## ValleyDepth 148.66818
## Slope_Height 133.94411
## Standardized_Height 131.22079
## Normalized_Height 113.14580
## SlopeHeight 93.60946
## Standardized_Height_hr 89.71376
## StandardizedHeight 89.69888
## parameters
## Maximum_Height_hr Maximum_Height_hr
## Vertical_Distance_to_Channel_Network Vertical_Distance_to_Channel_Network
## Relative_Slope_Position Relative_Slope_Position
## ValleyDepth ValleyDepth
## Slope_Height Slope_Height
## Standardized_Height Standardized_Height
## Normalized_Height Normalized_Height
## SlopeHeight SlopeHeight
## Standardized_Height_hr Standardized_Height_hr
## StandardizedHeight StandardizedHeight
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 92 4 0 4 0 5 2 8 0 0 4 1 2 2 0 0
## Ant 13 149 0 4 0 8 21 6 0 1 3 11 2 3 5 0
## CBD 0 0 135 0 13 0 0 0 6 7 0 0 12 1 22 0
## CD 9 2 2 93 0 17 14 2 0 1 3 0 12 5 2 5
## CSR 0 0 11 0 139 0 0 0 5 0 0 0 0 0 16 0
## DC 9 4 0 12 0 122 12 5 0 2 3 2 10 1 2 0
## GLD 14 23 1 24 0 13 327 20 0 2 10 0 8 5 4 0
## IMS 5 1 2 13 0 2 12 136 0 0 1 0 9 5 1 0
## ISR 0 0 5 3 10 2 0 0 124 1 2 0 18 2 18 18
## LD 3 0 9 7 0 4 1 11 0 154 14 0 4 10 4 0
## LT 10 10 0 13 0 0 5 6 0 3 124 2 0 9 1 1
## MrD 24 0 0 0 0 0 0 0 0 0 0 164 0 0 1 0
## MxD 0 0 8 6 1 13 0 2 22 2 3 0 99 4 7 7
## SB 0 2 9 1 0 1 0 0 7 5 11 0 0 122 17 0
## SD 0 2 8 1 6 1 0 0 3 6 2 0 7 9 76 0
## SSR 0 0 0 14 5 1 0 0 25 0 5 0 8 2 10 167
## TG 6 0 9 5 1 12 4 1 7 16 14 0 7 18 7 1
## WB 1 0 0 2 0 0 3 0 0 0 1 2 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 2 0
## CBD 6 0
## CD 8 0
## CSR 0 0
## DC 5 0
## GLD 4 2
## IMS 3 0
## ISR 23 0
## LD 13 2
## LT 15 2
## MrD 0 0
## MxD 11 0
## SB 12 0
## SD 14 0
## SSR 10 0
## TG 274 1
## WB 0 194
## [1] "classification error rate with altdata: 0.314918533604888"evaluateforwardCV_anyerror(mypath=paste(base,"ranfor_fw_5fold_10p_geomorphologie_beschreibung_heights_100pg",sep=""),kk = 1:5,endround = 10,error = "cverror",geheim="geheimerprederror",yrange=c(0,1))
## [1] "Prediction error at end is: 0.691094147582697"
## [2] "Prediction error at end is: 0.526717557251908"
## [3] "Prediction error at end is: 0.419338422391857"
## [4] "Prediction error at end is: 0.372519083969466"
## [5] "Prediction error at end is: 0.334860050890585"
## [6] "Prediction error at end is: 0.310941475826972"
## [7] "Prediction error at end is: 0.31501272264631"
## [8] "Prediction error at end is: 0.318575063613232"
## [9] "Prediction error at end is: 0.31704834605598"
## [10] "Prediction error at end is: 0.319083969465649"
## k 1
## 1 Vertical_Distance_to_Channel_Network
## 2 Maximum_Height_hr
## 3 Relative_Slope_Position
## 4 ValleyDepth
## 5 Normalized_Height
## 6 Slope_Height
## 7 Valley_Depth_hr
## 8 NormalizedHeight
## 9 Slope_Height_hr
## 10 Mid_Slope_Positon
## k 2
## 1 Standardized_Height
## 2 Maximum_Height_hr
## 3 Vertical_Distance_to_Channel_Network
## 4 Slope_Height
## 5 Relative_Slope_Position
## 6 ValleyDepth
## 7 NormalizedHeight
## 8 Valley_Depth_hr
## 9 SlopeHeight
## 10 Mid_Slope_Positon
## k 3
## 1 Vertical_Distance_to_Channel_Network
## 2 Maximum_Height_hr
## 3 Relative_Slope_Position
## 4 Standardized_Height
## 5 ValleyDepth
## 6 Slope_Height
## 7 SlopeHeight
## 8 Valley_Depth_hr
## 9 Slope_Height_hr
## 10 NormalizedHeight
## k 4
## 1 Vertical_Distance_to_Channel_Network
## 2 Maximum_Height_hr
## 3 Relative_Slope_Position
## 4 Standardized_Height
## 5 Slope_Height
## 6 ValleyDepth
## 7 Valley_Depth_hr
## 8 SlopeHeight
## 9 StandardizedHeight
## 10 Normalized_Height
## k 5
## 1 Vertical_Distance_to_Channel_Network
## 2 Maximum_Height_hr
## 3 Relative_Slope_Position
## 4 Normalized_Height
## 5 Slope_Height
## 6 ValleyDepth
## 7 NormalizedHeight
## 8 Valley_Depth_hr
## 9 Slope_Height_hr
## 10 SlopeHeight
## allchosen Freq
## 1 Maximum_Height_hr 5
## 5 Relative_Slope_Position 5
## 7 Slope_Height 5
## 11 ValleyDepth 5
## 12 Valley_Depth_hr 5
## 13 Vertical_Distance_to_Channel_Network 5
## 3 NormalizedHeight 4
## 6 SlopeHeight 4
## 4 Normalized_Height 3
## 8 Slope_Height_hr 3
## 10 Standardized_Height 3
## 2 Mid_Slope_Positon 2with same predictors as svm
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Maximum_Height_hr","ValleyDepth","Vertical_Distance_to_Channel_Network","SlopeHeight","Valley_Depth_hr","Normalized_Height"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.32824427480916 for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 95 4 0 4 0 6 4 4 0 1 4 0 3 2 0 0
## Ant 9 150 1 5 0 6 17 7 0 0 5 12 4 4 3 0
## CBD 0 0 133 0 15 1 0 0 10 4 2 0 7 5 30 0
## CD 11 4 1 87 0 14 10 3 2 2 3 0 11 4 3 9
## CSR 0 0 11 0 138 0 0 0 4 0 0 0 0 0 21 1
## DC 13 0 2 11 0 111 14 6 1 5 2 1 6 1 2 0
## GLD 13 20 2 33 0 20 324 29 0 1 13 5 9 7 3 0
## IMS 5 2 1 11 0 4 18 124 0 0 3 0 6 1 2 0
## ISR 0 0 11 4 10 1 0 0 116 6 0 0 24 1 23 13
## LD 1 1 7 6 0 7 1 13 0 146 12 0 5 8 1 0
## LT 6 3 0 10 0 0 3 2 0 2 123 0 1 12 2 0
## MrD 22 3 0 0 0 1 0 0 0 0 0 163 1 0 1 0
## MxD 0 1 1 9 0 16 1 2 24 4 4 0 91 5 9 6
## SB 1 3 9 1 0 0 1 0 5 5 10 1 2 119 19 0
## SD 1 6 5 1 5 0 0 0 5 6 0 0 3 7 56 2
## SSR 0 0 4 8 6 0 0 0 24 0 3 0 16 5 12 167
## TG 8 0 11 9 1 14 6 7 8 18 15 0 9 17 6 1
## WB 1 0 0 3 0 0 2 0 0 0 1 0 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 1 0
## CBD 8 0
## CD 8 0
## CSR 0 0
## DC 8 0
## GLD 3 2
## IMS 6 0
## ISR 25 0
## LD 15 0
## LT 15 2
## MrD 0 0
## MxD 12 0
## SB 13 0
## SD 8 1
## SSR 8 0
## TG 269 1
## WB 1 195
## [1] "classification error rate with altdata: 0.336303462321792"
## [1] "Kappa overall = 0.641616087330147"
## [1] "Tau overall = 0.643913981071043"
## [1] "mean quality = 0.502378933165069"
## [1] "The quality is 0.502378933165069"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.660897909166221"with own predictors:
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Vertical_Distance_to_Channel_Network","Maximum_Height_hr","Relative_Slope_Position","Standardized_Height","Slope_Height","ValleyDepth"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.304834605597964 for predictors Vertical_Distance_to_Channel_Network AND Maximum_Height_hr AND Relative_Slope_Position AND Standardized_Height AND Slope_Height AND ValleyDepth"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 89 7 0 5 0 8 4 5 0 1 5 5 6 1 0 0
## Ant 9 138 1 6 0 6 19 1 0 1 2 12 2 5 1 0
## CBD 0 0 135 0 15 0 0 0 6 6 0 0 11 4 25 0
## CD 10 4 1 87 0 9 9 3 0 0 6 0 9 4 5 2
## CSR 0 0 5 0 139 0 0 0 8 0 0 0 3 0 15 0
## DC 11 3 1 13 0 112 13 2 0 3 5 3 5 5 3 0
## GLD 9 20 4 26 0 15 328 20 0 1 7 4 7 4 5 0
## IMS 5 5 1 12 0 3 8 148 0 0 1 0 5 3 1 0
## ISR 0 0 8 0 7 1 0 0 129 1 3 0 15 0 18 13
## LD 1 0 9 8 1 16 0 11 1 156 6 0 1 12 3 0
## LT 12 7 0 13 0 1 5 6 0 2 129 0 1 11 4 1
## MrD 32 2 0 0 0 3 0 0 0 0 0 158 0 0 2 0
## MxD 0 0 8 6 0 7 1 1 19 1 6 0 101 4 6 11
## SB 1 3 5 1 0 3 3 0 2 3 9 0 1 116 21 2
## SD 1 6 7 4 9 0 3 0 6 8 2 0 8 11 72 1
## SSR 0 0 1 12 4 1 0 0 19 0 4 0 11 2 7 169
## TG 6 2 13 7 0 16 6 0 9 17 15 0 12 15 5 0
## WB 0 0 0 2 0 0 2 0 0 0 0 0 0 1 0 0
##
## altpreds TG WB
## AD 1 0
## Ant 1 0
## CBD 7 0
## CD 6 0
## CSR 1 0
## DC 9 0
## GLD 5 0
## IMS 4 1
## ISR 20 0
## LD 13 0
## LT 15 4
## MrD 0 2
## MxD 15 0
## SB 12 0
## SD 13 1
## SSR 6 0
## TG 270 1
## WB 2 192
## [1] "classification error rate with altdata: 0.320773930753564"
## [1] "Kappa overall = 0.658342117634389"
## [1] "Tau overall = 0.660357014496226"
## [1] "mean quality = 0.518108080063575"
## [1] "The quality is 0.518108080063575"
## [1] "######### Cramer's V = 0.672932773473784"RandomForest n=200
## [1] "Prediction error at end is: 0.629581368211212"
## [2] "Prediction error at end is: 0.495926808317531"
## [3] "Prediction error at end is: 0.415476896646732"
## [4] "Prediction error at end is: 0.357433104811916"
## [5] "Prediction error at end is: 0.32714250984587"
## [6] "Prediction error at end is: 0.289466297142672"
## [7] "Prediction error at end is: 0.269100662874184"
## [8] "Prediction error at end is: 0.266295521952643"
## [9] "Prediction error at end is: 0.269349929498712"
## [10] "Prediction error at end is: 0.267058880731269"
## k 1
## 1 Standardized_Height
## 2 Normalized_Height
## 3 Vertical_Distance_to_Channel_Network
## 4 Slope_Height
## 5 Relative_Slope_Position
## 6 Maximum_Height_hr
## 7 ValleyDepth
## 8 Standardized_Height_hr
## 9 SlopeHeight
## 10 StandardizedHeight
## k 2
## 1 Standardized_Height
## 2 Normalized_Height
## 3 Vertical_Distance_to_Channel_Network
## 4 Relative_Slope_Position
## 5 Maximum_Height_hr
## 6 Slope_Height
## 7 ValleyDepth
## 8 StandardizedHeight
## 9 MidSlope_Positon_hr
## 10 Valley_Depth_hr
## k 3
## 1 Standardized_Height
## 2 Maximum_Height_hr
## 3 Relative_Slope_Position
## 4 Vertical_Distance_to_Channel_Network
## 5 Slope_Height
## 6 ValleyDepth
## 7 Normalized_Height
## 8 Standardized_Height_hr
## 9 Valley_Depth_hr
## 10 SlopeHeight
## k 4
## 1 Standardized_Height
## 2 Normalized_Height
## 3 Vertical_Distance_to_Channel_Network
## 4 Slope_Height
## 5 Relative_Slope_Position
## 6 Maximum_Height_hr
## 7 ValleyDepth
## 8 SlopeHeight
## 9 Valley_Depth_hr
## 10 Normalized_Height_hr
## k 5
## 1 Standardized_Height
## 2 Normalized_Height
## 3 Vertical_Distance_to_Channel_Network
## 4 Slope_Height
## 5 Relative_Slope_Position
## 6 Maximum_Height_hr
## 7 ValleyDepth
## 8 Valley_Depth_hr
## 9 Mid_Slope_Positon
## 10 NormalizedHeight
## allchosen Freq
## 1 Maximum_Height_hr 5
## 5 Normalized_Height 5
## 7 Relative_Slope_Position 5
## 9 Slope_Height 5
## 11 Standardized_Height 5
## 13 ValleyDepth 5
## 15 Vertical_Distance_to_Channel_Network 5
## 14 Valley_Depth_hr 4
## 8 SlopeHeight 3
## 10 StandardizedHeight 2
## 12 Standardized_Height_hr 2predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Vertical_Distance_to_Channel_Network","Maximum_Height_hr","Relative_Slope_Position","Standardized_Height","Slope_Height","ValleyDepth"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.256109979633401 for predictors Vertical_Distance_to_Channel_Network AND Maximum_Height_hr AND Relative_Slope_Position AND Standardized_Height AND Slope_Height AND ValleyDepth"
## [1] "Kappa overall = 1"
## [1] "Tau overall = 1"
## [1] "mean quality = 1"
## [1] "The quality is 1"
## [1] "######### Cramer's V = 1"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 43 1 0 5 0 8 0 0 0 0 2 14 0 1 0 0
## Ant 8 80 0 4 0 5 1 3 0 0 1 1 1 2 2 0
## CBD 0 0 75 0 4 0 0 0 5 1 0 0 2 3 9 1
## CD 5 0 0 50 0 6 3 0 0 1 4 0 2 1 1 0
## CSR 0 0 3 0 72 0 0 0 2 0 0 0 2 0 9 0
## DC 6 2 1 7 0 63 3 2 0 5 1 1 2 1 0 0
## GLD 8 5 2 10 0 8 182 7 0 1 5 0 5 1 3 0
## IMS 0 0 0 5 0 0 3 84 0 0 1 0 4 1 1 0
## ISR 0 0 2 2 1 0 0 0 75 0 2 0 8 2 5 4
## LD 2 0 4 0 0 3 0 0 0 87 4 0 0 5 6 0
## LT 5 0 0 4 0 1 3 1 0 0 70 0 2 3 2 1
## MrD 12 11 0 0 0 1 2 0 0 0 0 67 0 1 0 0
## MxD 0 0 4 5 0 3 0 0 3 0 2 0 62 2 5 0
## SB 0 0 5 1 0 0 1 0 0 2 4 0 0 57 13 0
## SD 0 0 3 0 13 0 1 0 4 1 1 0 5 8 38 0
## SSR 0 0 0 4 1 0 0 0 7 0 0 0 3 4 2 94
## TG 2 0 2 4 0 2 1 0 4 2 3 0 2 8 3 0
## WB 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
##
## altpreds TG WB
## AD 1 0
## Ant 1 0
## CBD 1 0
## CD 4 0
## CSR 2 0
## DC 8 0
## GLD 2 0
## IMS 1 0
## ISR 15 0
## LD 9 0
## LT 7 0
## MrD 0 0
## MxD 3 0
## SB 8 2
## SD 2 0
## SSR 3 0
## TG 134 0
## WB 0 98
## [1] "classification error rate with altdata: 0.27175572519084"
## [1] "Kappa overall = 0.710708986435185"
## [1] "Tau overall = 0.712258643915581"
## [1] "mean quality = 0.573752690176201"
## [1] "The quality is 0.573752690176201"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = 0.722370767199137"ALLGEOMS
SVM n=100
## [1] "Prediction error at end is: 0.81323155216285"
## [2] "Prediction error at end is: 0.762849872773537"
## [3] "Prediction error at end is: 0.759287531806616"
## [4] "Prediction error at end is: 0.766412213740458"
## [5] "Prediction error at end is: 0.767430025445293"
## [6] "Prediction error at end is: 0.773027989821883"
## [7] "Prediction error at end is: 0.768956743002545"
## [8] "Prediction error at end is: 0.769465648854962"
## [9] "Prediction error at end is: 0.772519083969466"
## [10] "Prediction error at end is: 0.762849872773537"
## k 1 k 2
## 1 geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl2_L1500m
## 2 geom_DTM_50m_avg_fl10_L150m geom_DTM_50m_avg_fl10_L250m
## 3 geom_10m_fl10_L8 geom_DTM_50m_avg_fl1_L150m
## 4 geom_DTM_50m_avg_fl2_L1300m geom_DTM_50m_avg_fl3_L1500m
## 5 geom_10m_fl2_L4 geom_DTM_50m_avg_fl1_L1000m
## 6 geom_DTM_50m_avg_fl4_L200m geom_DTM_50m_avg_fl4_L800m
## 7 geom_10m_fl1_L47 geom_10m_fl1_L90
## 8 geom_10m_fl4_L30 geom_DTM_50m_avg_fl3_L900m
## 9 geom_DTM_50m_avg_fl1_L200m geom_10m_fl8_L110
## 10 geom_10m_fl1_L130 geom_10m_fl3_L120
## k 3 k 4
## 1 geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl1_L1500m
## 2 geom_DTM_50m_avg_fl10_L150m geom_DTM_50m_avg_fl10_L150m
## 3 geom_10m_fl10_L3 geom_10m_fl2_L4
## 4 geom_10m_fl2_L110 geom_10m_fl1_L140
## 5 geom_10m_fl3_L6 geom_DTM_50m_avg_fl8_L1400m
## 6 geom_10m_fl10_L70 geom_DTM_50m_avg_fl2_L400m
## 7 geom_DTM_50m_avg_fl4_L500m geom_10m_fl10_L7
## 8 geom_10m_fl3_L46 geom_10m_fl4_L12
## 9 geom_DTM_50m_avg_fl4_L1500m geom_dtm_10m_hyd_fl5_L100
## 10 geom_DTM_50m_avg_fl1_L300m geom_DTM_50m_avg_fl1_L150m
## k 5
## 1 geom_DTM_50m_avg_fl1_L1500m
## 2 geom_DTM_50m_avg_fl10_L150m
## 3 geom_10m_fl10_L3
## 4 geom_10m_fl2_L130
## 5 geom_10m_fl1_L70
## 6 geom_DTM_50m_avg_fl1_L150m
## 7 geom_10m_fl10_L7
## 8 geom_DTM_50m_avg_fl4_L600m
## 9 geom_10m_fl10_L11
## 10 geom_DTM_50m_avg_fl1_L250m
## allchosen Freq
## 21 geom_DTM_50m_avg_fl10_L150m 4
## 24 geom_DTM_50m_avg_fl1_L1500m 4
## 25 geom_DTM_50m_avg_fl1_L150m 3
## 2 geom_10m_fl10_L3 2
## 3 geom_10m_fl10_L7 2
## 13 geom_10m_fl2_L4 2## [1] "10fold cv-error: 0.750127226463104 for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L150m"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 15 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0
## Ant 0 5 0 0 0 0 0 0 0 0 0 0 0 2 1 0 2
## CBD 2 0 9 2 5 0 11 2 6 3 1 0 9 2 10 2 3
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 9 20 0 30 0 68 16 7 0 28 30 2 10 2 0 4 24
## GLD 11 14 1 25 8 5 64 18 13 12 22 0 22 17 5 12 15
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 1 2 0 8 0 5 23 2 0 10 23 0 4 0 1 9 10
## MrD 25 12 0 0 0 0 0 0 0 0 0 62 0 0 0 0 0
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 0 11 0 0 1 0 16 8 0 0 6 0 0 21 1 3 3
## SD 0 0 5 2 6 0 4 2 7 2 0 0 6 1 7 7 1
## SSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0
## TG 15 19 86 15 71 7 48 58 74 43 15 0 49 55 75 60 143
## WB 13 16 0 19 0 15 18 0 0 2 3 14 0 0 0 0 0
##
## preds WB
## AD 0
## Ant 0
## CBD 0
## CD 0
## CSR 0
## DC 2
## GLD 2
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 5
## MxD 0
## SB 0
## SD 0
## SSR 0
## TG 0
## WB 91
## [1] "Kappa overall = 0.196490341943895"
## [1] "Tau overall = 0.216524472384374"
## [1] "mean quality = 0.117602056036468"
## [1] "The quality is 0.117602056036468"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 30 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0
## Ant 0 5 0 0 0 0 0 1 0 0 2 0 0 10 2 0
## CBD 3 2 12 3 10 2 27 7 16 4 3 0 15 6 21 8
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 15 48 0 59 0 114 22 10 0 70 41 5 21 2 3 3
## GLD 22 31 8 36 11 18 92 38 24 18 57 1 50 25 13 35
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 6 3 0 15 0 15 37 4 0 13 24 0 7 4 1 12
## MrD 49 20 0 0 0 2 0 0 0 0 0 116 0 0 0 0
## MxD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SB 3 22 0 1 10 1 20 15 1 1 17 0 0 36 2 6
## SD 1 0 6 4 11 0 2 15 13 1 1 0 10 3 2 11
## SSR 0 1 0 0 0 0 0 0 0 0 1 0 0 3 0 0
## TG 24 41 173 50 133 20 143 107 145 91 42 0 93 107 148 124
## WB 33 24 0 34 0 29 58 0 0 2 12 41 2 2 1 0
##
## altpreds TG WB
## AD 0 0
## Ant 6 0
## CBD 11 0
## CD 0 0
## CSR 0 0
## DC 42 9
## GLD 32 2
## IMS 0 0
## ISR 0 0
## LD 0 0
## LT 20 0
## MrD 0 4
## MxD 0 0
## SB 1 0
## SD 2 0
## SSR 0 0
## TG 285 1
## WB 1 185
## [1] "classification error rate with altdata: 0.770621181262729"
## [1] "Kappa overall = 0.162635905524366"
## [1] "Tau overall = 0.184048161015934"
## [1] "mean quality = 0.0971995432521506"
## [1] "The quality is 0.0971995432521506"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"SVM n=200
## [1] "Prediction error at end is: 0.824595387432943"
## [2] "Prediction error at end is: 0.784106902643393"
## [3] "Prediction error at end is: 0.775198132931395"
## [4] "Prediction error at end is: 0.765271551514562"
## [5] "Prediction error at end is: 0.755341728659179"
## [6] "Prediction error at end is: 0.752034164762322"
## [7] "Prediction error at end is: 0.754578045736698"
## [8] "Prediction error at end is: 0.752030923323771"
## [9] "Prediction error at end is: 0.742612275327791"
## [10] "Prediction error at end is: 0.742105314338503"
## k 1 k 2
## 1 geom_DTM_50m_avg_fl1_L1400m geom_DTM_50m_avg_fl1_L1400m
## 2 geom_DTM_50m_avg_fl10_L1500m geom_DTM_50m_avg_fl10_L1500m
## 3 geom_DTM_50m_avg_fl2_L150m geom_10m_fl10_L7
## 4 geom_DTM_50m_avg_fl10_L300m geom_DTM_50m_avg_fl2_L300m
## 5 geom_DTM_50m_avg_fl4_L1500m geom_10m_fl1_L120
## 6 geom_DTM_50m_avg_fl8_L1500m geom_DTM_50m_avg_fl1_L1500m
## 7 geom_DTM_50m_avg_fl2_L1500m geom_10m_fl2_L5
## 8 geom_DTM_50m_avg_fl2_L250m geom_10m_fl8_L40
## 9 geom_10m_fl8_L16 geom_10m_fl10_L14
## 10 geom_10m_fl1_L12 geom_DTM_50m_avg_fl2_L500m
## k 3 k 4
## 1 geom_10m_fl2_L150 geom_DTM_50m_avg_fl1_L1400m
## 2 geom_10m_fl10_L12 geom_DTM_50m_avg_fl10_L150m
## 3 geom_DTM_50m_avg_fl10_L150m geom_10m_fl10_L11
## 4 geom_10m_fl1_L5 geom_DTM_50m_avg_fl1_L600m
## 5 geom_DTM_50m_avg_fl2_L300m geom_DTM_50m_avg_fl1_L150m
## 6 geom_DTM_50m_avg_fl10_L1500m geom_DTM_50m_avg_fl4_L1500m
## 7 geom_DTM_50m_avg_fl10_L500m geom_10m_fl1_L110
## 8 geom_dtm_10m_hyd_fl5_L31 geom_10m_fl10_L3
## 9 geom_DTM_50m_avg_fl3_L1500m geom_DTM_50m_avg_fl10_L800m
## 10 geom_10m_fl10_L3 geom_DTM_50m_avg_fl4_L250m
## k 5
## 1 geom_DTM_50m_avg_fl1_L1400m
## 2 geom_DTM_50m_avg_fl10_L150m
## 3 geom_10m_fl10_L4
## 4 geom_DTM_50m_avg_fl4_L250m
## 5 geom_DTM_50m_avg_fl10_L600m
## 6 geom_DTM_50m_avg_fl3_L1400m
## 7 geom_DTM_50m_avg_fl1_L150m
## 8 geom_DTM_50m_avg_fl2_L1000m
## 9 geom_10m_fl1_L6
## 10 geom_10m_fl10_L14
## allchosen Freq
## 23 geom_DTM_50m_avg_fl1_L1400m 4
## 17 geom_DTM_50m_avg_fl10_L1500m 3
## 18 geom_DTM_50m_avg_fl10_L150m 3
## 3 geom_10m_fl10_L14 2
## 4 geom_10m_fl10_L3 2
## 25 geom_DTM_50m_avg_fl1_L150m 2
## 31 geom_DTM_50m_avg_fl2_L300m 2
## 35 geom_DTM_50m_avg_fl4_L1500m 2
## 36 geom_DTM_50m_avg_fl4_L250m 2## [1] "10fold cv-error: 0.764002036659878 for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L150m"
##
## preds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR TG
## AD 30 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0
## Ant 6 23 0 29 0 25 8 1 0 16 7 5 5 0 0 0 14
## CBD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 9 25 0 30 0 89 14 9 0 54 34 0 16 2 3 3 28
## GLD 15 14 1 22 6 18 109 28 4 25 68 0 15 25 5 30 46
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 49 20 0 0 0 2 0 0 0 0 0 116 0 0 0 0 0
## MxD 14 20 13 33 16 15 22 29 33 7 14 1 52 8 12 28 8
## SB 3 28 0 1 10 1 20 16 1 1 20 0 0 49 4 6 7
## SD 3 2 12 3 10 2 27 7 16 4 3 0 15 5 20 8 11
## SSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TG 24 41 173 50 133 20 143 107 145 91 42 0 93 107 148 124 285
## WB 33 24 0 34 0 29 58 0 0 2 12 41 2 2 1 0 1
##
## preds WB
## AD 0
## Ant 7
## CBD 0
## CD 0
## CSR 0
## DC 2
## GLD 0
## IMS 0
## ISR 0
## LD 0
## LT 0
## MrD 4
## MxD 2
## SB 0
## SD 0
## SSR 0
## TG 1
## WB 185
## [1] "Kappa overall = 0.1794447323937"
## [1] "Tau overall = 0.199412962741105"
## [1] "mean quality = 0.107211034953502"
## [1] "The quality is 0.107211034953502"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 15 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0
## Ant 8 12 0 9 0 19 7 1 0 6 9 1 1 0 0 0
## CBD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 1 8 0 21 0 49 9 6 0 22 21 1 9 2 0 4
## GLD 2 9 1 13 4 6 67 15 1 13 40 0 9 13 3 15
## IMS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## ISR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MrD 25 12 0 0 0 0 0 0 0 0 0 62 0 0 0 0
## MxD 10 7 5 22 10 4 24 7 19 11 5 0 23 5 10 13
## SB 0 16 0 0 1 0 16 8 0 0 6 0 0 23 2 6
## SD 2 0 9 2 5 0 11 2 6 3 1 0 9 2 10 2
## SSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TG 15 19 86 15 71 7 48 58 74 43 15 0 49 55 75 60
## WB 13 16 0 19 0 15 18 0 0 2 3 14 0 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 10 2
## CBD 0 0
## CD 0 0
## CSR 0 0
## DC 14 0
## GLD 21 1
## IMS 0 0
## ISR 0 0
## LD 0 0
## LT 0 0
## MrD 0 5
## MxD 5 1
## SB 5 0
## SD 3 0
## SSR 0 0
## TG 143 0
## WB 0 91
## [1] "classification error rate with altdata: 0.748091603053435"
## [1] "Kappa overall = 0.188434015222394"
## [1] "Tau overall = 0.207903008531657"
## [1] "mean quality = 0.113132569009515"
## [1] "The quality is 0.113132569009515"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=7,altdata=twohundred,legend=geolegendeng)## [1] "OBB error with all predictors of allgeoms is 0.623409669211196"
## MeanDecreaseGini parameters
## geom_10m_fl10_L3 12.049847 geom_10m_fl10_L3
## geom_DTM_50m_avg_fl1_L150m 11.728523 geom_DTM_50m_avg_fl1_L150m
## geom_DTM_50m_avg_fl10_L200m 11.350796 geom_DTM_50m_avg_fl10_L200m
## geom_DTM_50m_avg_fl10_L150m 11.189319 geom_DTM_50m_avg_fl10_L150m
## geom_DTM_50m_avg_fl1_L200m 10.745587 geom_DTM_50m_avg_fl1_L200m
## geom_10m_fl1_L4 10.559035 geom_10m_fl1_L4
## geom_10m_fl10_L4 10.169567 geom_10m_fl10_L4
## geom_10m_fl1_L3 9.850641 geom_10m_fl1_L3
## geom_DTM_50m_avg_fl1_L250m 9.710074 geom_DTM_50m_avg_fl1_L250m
## geom_10m_fl1_L5 9.260687 geom_10m_fl1_L5
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 57 9 4 5 0 4 3 2 0 1 1 33 2 1 1 3
## Ant 7 67 2 5 2 3 6 3 1 1 1 14 2 7 1 3
## CBD 8 9 92 9 49 6 46 17 36 38 8 0 32 33 64 45
## CD 10 11 0 65 2 23 14 2 2 5 17 5 11 2 2 4
## CSR 3 6 10 1 36 0 10 9 22 3 1 0 8 18 15 18
## DC 24 16 0 24 0 104 12 1 0 17 12 3 7 0 1 0
## GLD 12 19 2 24 1 10 184 18 8 19 41 1 18 24 12 6
## IMS 5 4 9 10 2 3 15 69 12 9 6 0 17 8 8 7
## ISR 1 3 22 4 16 1 8 15 36 3 3 0 13 12 18 19
## LD 6 4 6 10 4 4 15 4 4 51 8 1 7 6 5 4
## LT 3 3 1 3 0 18 16 4 1 17 60 0 2 4 1 0
## MrD 32 4 0 0 0 5 0 0 0 0 0 105 0 0 0 0
## MxD 4 4 6 13 11 6 20 14 17 5 8 0 42 3 10 17
## SB 6 12 2 5 8 4 13 2 5 2 14 1 1 35 5 10
## SD 0 2 15 4 18 0 4 13 14 2 5 0 8 11 18 7
## SSR 0 5 7 2 7 1 3 8 9 4 1 0 4 11 10 36
## TG 3 17 21 17 19 8 29 16 32 23 14 0 24 22 22 20
## WB 5 2 0 1 0 1 3 0 0 0 0 19 0 1 0 0
##
## altpreds TG WB
## AD 1 4
## Ant 6 0
## CBD 70 0
## CD 10 2
## CSR 14 0
## DC 12 1
## GLD 28 7
## IMS 14 0
## ISR 26 0
## LD 14 0
## LT 18 0
## MrD 0 2
## MxD 17 0
## SB 13 0
## SD 6 0
## SSR 8 0
## TG 143 1
## WB 0 184
## [1] "classification error rate with altdata: 0.647657841140529"RandomForest n=100
## [1] "Prediction error at end is: 0.816793893129771"
## [2] "Prediction error at end is: 0.772519083969466"
## [3] "Prediction error at end is: 0.7735368956743"
## [4] "Prediction error at end is: 0.734351145038168"
## [5] "Prediction error at end is: 0.722137404580153"
## [6] "Prediction error at end is: 0.716539440203562"
## [7] "Prediction error at end is: 0.700254452926209"
## [8] "Prediction error at end is: 0.690076335877863"
## [9] "Prediction error at end is: 0.689058524173028"
## [10] "Prediction error at end is: 0.695674300254453"
## k 1 k 2
## 1 geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl1_L1500m
## 2 geom_DTM_50m_avg_fl10_L1100m geom_10m_fl10_L60
## 3 geom_DTM_50m_avg_fl3_L600m geom_DTM_50m_avg_fl10_L1500m
## 4 geom_DTM_50m_avg_fl8_L200m geom_DTM_50m_avg_fl4_L600m
## 5 geom_DTM_50m_avg_fl4_L1100m geom_DTM_50m_avg_fl4_L1500m
## 6 geom_10m_fl1_L38 geom_DTM_50m_avg_fl2_L400m
## 7 geom_DTM_50m_avg_fl4_L250m geom_DTM_50m_avg_fl10_L250m
## 8 geom_DTM_50m_avg_fl10_L500m geom_DTM_50m_avg_fl3_L200m
## 9 geom_DTM_50m_avg_fl1_L1000m geom_DTM_50m_avg_fl10_L500m
## 10 geom_10m_fl3_L39 geom_DTM_50m_avg_fl4_L1000m
## k 3 k 4
## 1 geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl2_L1500m
## 2 geom_DTM_50m_avg_fl10_L1100m geom_DTM_50m_avg_fl10_L400m
## 3 geom_DTM_50m_avg_fl2_L600m geom_DTM_50m_avg_fl1_L1200m
## 4 geom_10m_fl2_L22 geom_10m_fl1_L4
## 5 geom_DTM_50m_avg_fl4_L1000m geom_DTM_50m_avg_fl4_L1000m
## 6 geom_DTM_50m_avg_fl8_L400m geom_DTM_50m_avg_fl8_L1200m
## 7 geom_DTM_50m_avg_fl8_L1200m geom_10m_fl2_L16
## 8 geom_10m_fl1_L4 geom_DTM_50m_avg_fl10_L200m
## 9 geom_dtm_10m_hyd_fl5_L130 geom_DTM_50m_avg_fl10_L600m
## 10 geom_DTM_50m_avg_fl3_L150m geom_DTM_50m_avg_fl2_L200m
## k 5
## 1 geom_DTM_50m_avg_fl1_L1500m
## 2 geom_DTM_50m_avg_fl10_L400m
## 3 geom_10m_fl4_L48
## 4 geom_DTM_50m_avg_fl4_L500m
## 5 geom_DTM_50m_avg_fl10_L1400m
## 6 geom_DTM_50m_avg_fl1_L300m
## 7 geom_DTM_50m_avg_fl8_L200m
## 8 geom_DTM_50m_avg_fl2_L1500m
## 9 geom_DTM_50m_avg_fl4_L1500m
## 10 geom_DTM_50m_avg_fl8_L250m
## allchosen Freq
## 19 geom_DTM_50m_avg_fl1_L1500m 4
## 28 geom_DTM_50m_avg_fl4_L1000m 3
## 3 geom_10m_fl1_L4 2
## 9 geom_DTM_50m_avg_fl10_L1100m 2
## 14 geom_DTM_50m_avg_fl10_L400m 2
## 15 geom_DTM_50m_avg_fl10_L500m 2
## 21 geom_DTM_50m_avg_fl2_L1500m 2
## 30 geom_DTM_50m_avg_fl4_L1500m 2
## 34 geom_DTM_50m_avg_fl8_L1200m 2
## 35 geom_DTM_50m_avg_fl8_L200m 2predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_DTM_50m_avg_fl1_L1500m","geom_DTM_50m_avg_fl10_L400m"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.750636132315522 for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L400m"
## [1] "Kappa overall = 0.226580877711267"
## [1] "Tau overall = 0.242388863942524"
## [1] "mean quality = 0.140203298701791"
## [1] "The quality is 0.140203298701791"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 39 19 0 11 0 9 13 3 0 2 4 20 4 1 2 0
## Ant 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CBD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CD 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 16 36 0 41 0 102 10 6 0 62 39 3 6 1 2 3
## GLD 8 27 8 32 11 17 119 46 14 21 56 2 29 24 20 15
## IMS 1 6 0 15 0 17 10 5 0 15 5 0 9 1 5 4
## ISR 5 5 29 18 24 3 38 32 56 10 17 0 23 36 29 33
## LD 2 0 0 4 0 0 8 1 0 9 5 0 3 2 0 0
## LT 6 3 0 11 0 14 17 0 0 9 24 0 6 7 0 9
## MrD 49 20 0 2 0 2 0 0 0 1 0 116 0 0 0 0
## MxD 3 0 12 6 18 2 6 12 20 3 2 1 28 4 8 22
## SB 6 24 0 0 8 0 18 6 0 0 8 0 0 28 0 4
## SD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SSR 0 2 0 0 0 0 0 0 0 1 2 0 1 4 0 6
## TG 21 39 150 31 114 12 111 86 109 67 28 0 89 89 126 103
## WB 29 16 0 31 0 22 51 0 0 0 10 40 0 1 1 0
##
## altpreds TG WB
## AD 1 13
## Ant 0 0
## CBD 0 0
## CD 0 0
## CSR 0 0
## DC 32 7
## GLD 32 2
## IMS 13 0
## ISR 31 1
## LD 4 0
## LT 18 0
## MrD 0 4
## MxD 6 0
## SB 1 0
## SD 0 0
## SSR 1 0
## TG 260 0
## WB 1 174
## [1] "classification error rate with altdata: 0.754073319755601"
## [1] "Kappa overall = 0.184412025186994"
## [1] "Tau overall = 0.201569426141129"
## [1] "mean quality = 0.111793910887642"
## [1] "The quality is 0.111793910887642"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"Für höchste importance:
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_10m_fl10_L3","geom_DTM_50m_avg_fl10_L200m"),altdata=twohundred,legend=geolegendeng)## [1] "OOB-error: 0.830534351145038 for predictors geom_10m_fl10_L3 AND geom_DTM_50m_avg_fl10_L200m"
## [1] "Kappa overall = 0.141536576379661"
## [1] "Tau overall = 0.147013920071846"
## [1] "mean quality = 0.0663663073559193"
## [1] "The quality is 0.0663663073559193"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Ant 0 1 0 0 3 0 3 3 0 0 1 0 1 1 0 3
## CBD 18 21 150 28 108 8 98 58 116 61 23 0 64 95 122 92
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 1 0 0 2 2 1 0 0 3 0 0 0 2 1 1 1
## DC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## GLD 16 39 3 29 6 18 76 38 2 61 46 0 30 31 13 19
## IMS 3 2 2 2 2 1 16 11 1 4 3 0 10 3 3 6
## ISR 2 9 28 10 37 1 25 22 57 4 13 0 35 24 31 37
## LD 1 1 0 4 0 6 5 2 0 7 3 0 1 2 0 1
## LT 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## MrD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## MxD 1 0 0 1 0 3 1 0 2 0 0 0 7 1 1 3
## SB 4 5 0 0 4 0 11 6 3 0 2 0 2 8 1 1
## SD 1 2 14 3 12 0 6 21 11 2 1 0 11 4 11 12
## SSR 1 2 0 0 0 0 1 2 0 0 1 0 0 1 0 2
## TG 6 33 2 31 1 16 31 27 4 9 7 1 15 10 6 12
## WB 132 81 0 91 0 147 128 7 0 52 100 181 20 16 4 10
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 165 0
## CD 0 0
## CSR 0 0
## DC 0 0
## GLD 59 1
## IMS 9 1
## ISR 27 0
## LD 3 0
## LT 0 0
## MrD 0 0
## MxD 0 0
## SB 2 0
## SD 7 0
## SSR 1 0
## TG 71 3
## WB 56 196
## [1] "classification error rate with altdata: 0.847505091649694"
## [1] "Kappa overall = 0.0976050460037231"
## [1] "Tau overall = 0.102641667665029"
## [1] "mean quality = 0.0444539070841878"
## [1] "The quality is 0.0444539070841878"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"RandomForest n=200
## [1] "Prediction error at end is: 0.819756243821008"
## [2] "Prediction error at end is: 0.768330172930747"
## [3] "Prediction error at end is: 0.748220288163887"
## [4] "Prediction error at end is: 0.714099609406655"
## [5] "Prediction error at end is: 0.703153919709567"
## [6] "Prediction error at end is: 0.692716163433332"
## [7] "Prediction error at end is: 0.684315651286041"
## [8] "Prediction error at end is: 0.679731608888025"
## [9] "Prediction error at end is: 0.670055914814995"
## [10] "Prediction error at end is: 0.666241065784995"
## k 1 k 2
## 1 geom_DTM_50m_avg_fl1_L1300m geom_DTM_50m_avg_fl1_L1400m
## 2 geom_DTM_50m_avg_fl10_L1400m geom_DTM_50m_avg_fl10_L1500m
## 3 geom_DTM_50m_avg_fl3_L500m geom_DTM_50m_avg_fl3_L500m
## 4 geom_DTM_50m_avg_fl4_L250m geom_DTM_50m_avg_fl8_L250m
## 5 geom_DTM_50m_avg_fl2_L1400m geom_DTM_50m_avg_fl1_L150m
## 6 geom_DTM_50m_avg_fl1_L400m geom_DTM_50m_avg_fl10_L150m
## 7 geom_10m_fl10_L6 geom_DTM_50m_avg_fl8_L1200m
## 8 geom_DTM_50m_avg_fl1_L200m geom_DTM_50m_avg_fl4_L800m
## 9 geom_DTM_50m_avg_fl4_L1400m geom_DTM_50m_avg_fl4_L1500m
## 10 geom_DTM_50m_avg_fl3_L200m geom_DTM_50m_avg_fl2_L700m
## k 3 k 4
## 1 geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl1_L1300m
## 2 geom_DTM_50m_avg_fl8_L400m geom_DTM_50m_avg_fl10_L1500m
## 3 geom_DTM_50m_avg_fl1_L200m geom_DTM_50m_avg_fl2_L900m
## 4 geom_DTM_50m_avg_fl3_L400m geom_DTM_50m_avg_fl4_L500m
## 5 geom_DTM_50m_avg_fl10_L1300m geom_10m_fl10_L7
## 6 geom_DTM_50m_avg_fl10_L150m geom_DTM_50m_avg_fl10_L600m
## 7 geom_DTM_50m_avg_fl4_L1500m geom_DTM_50m_avg_fl1_L150m
## 8 geom_DTM_50m_avg_fl2_L300m geom_10m_fl4_L120
## 9 geom_DTM_50m_avg_fl4_L1100m geom_DTM_50m_avg_fl4_L1500m
## 10 geom_DTM_50m_avg_fl4_L300m geom_DTM_50m_avg_fl8_L1100m
## k 5
## 1 geom_DTM_50m_avg_fl1_L1400m
## 2 geom_DTM_50m_avg_fl10_L600m
## 3 geom_DTM_50m_avg_fl4_L250m
## 4 geom_DTM_50m_avg_fl10_L1300m
## 5 geom_DTM_50m_avg_fl1_L250m
## 6 geom_DTM_50m_avg_fl8_L500m
## 7 geom_DTM_50m_avg_fl8_L250m
## 8 geom_DTM_50m_avg_fl4_L700m
## 9 geom_DTM_50m_avg_fl1_L1000m
## 10 geom_DTM_50m_avg_fl2_L500m
## allchosen Freq
## 27 geom_DTM_50m_avg_fl4_L1500m 3
## 4 geom_DTM_50m_avg_fl10_L1300m 2
## 6 geom_DTM_50m_avg_fl10_L1500m 2
## 7 geom_DTM_50m_avg_fl10_L150m 2
## 8 geom_DTM_50m_avg_fl10_L600m 2
## 10 geom_DTM_50m_avg_fl1_L1300m 2
## 11 geom_DTM_50m_avg_fl1_L1400m 2
## 13 geom_DTM_50m_avg_fl1_L150m 2
## 14 geom_DTM_50m_avg_fl1_L200m 2
## 24 geom_DTM_50m_avg_fl3_L500m 2
## 28 geom_DTM_50m_avg_fl4_L250m 2
## 35 geom_DTM_50m_avg_fl8_L250m 2predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("geom_DTM_50m_avg_fl1_L1300m","geom_DTM_50m_avg_fl10_L1500m"),altdata=onehundred,legend=geolegendeng)## [1] "OOB-error: 0.75 for predictors geom_DTM_50m_avg_fl1_L1300m AND geom_DTM_50m_avg_fl10_L1500m"
## [1] "Kappa overall = 0.19829476701871"
## [1] "Tau overall = 0.213699532766263"
## [1] "mean quality = 0.117860006104573"
## [1] "The quality is 0.117860006104573"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"
##
## altpreds AD Ant CBD CD CSR DC GLD IMS ISR LD LT MrD MxD SB SD SSR
## AD 20 2 0 0 0 0 0 0 0 0 0 17 0 0 0 0
## Ant 0 0 0 0 1 0 2 0 0 0 0 0 0 2 0 0
## CBD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## CSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DC 6 8 1 24 0 37 17 12 0 25 5 0 7 2 3 0
## GLD 4 6 11 13 2 0 74 26 20 14 30 1 11 13 13 17
## IMS 2 4 0 1 0 2 3 16 0 0 2 0 2 2 2 2
## ISR 2 4 1 9 2 0 7 2 9 0 1 0 6 3 2 6
## LD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## LT 5 14 0 13 0 35 22 0 0 20 45 1 2 0 0 0
## MrD 21 10 0 0 0 1 0 0 0 0 0 53 0 0 0 0
## MxD 1 0 9 2 13 0 7 3 14 5 1 0 19 4 13 9
## SB 0 18 12 2 21 1 17 17 13 1 1 0 7 44 11 25
## SD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## SSR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## TG 11 18 67 14 52 12 27 20 44 33 12 0 44 30 56 41
## WB 19 15 0 23 0 12 24 1 0 2 3 12 2 0 0 0
##
## altpreds TG WB
## AD 0 0
## Ant 0 0
## CBD 0 0
## CD 0 0
## CSR 0 0
## DC 11 1
## GLD 21 1
## IMS 5 0
## ISR 2 0
## LD 0 0
## LT 17 0
## MrD 0 18
## MxD 2 0
## SB 29 0
## SD 0 0
## SSR 0 0
## TG 114 0
## WB 0 80
## [1] "classification error rate with altdata: 0.739949109414758"
## [1] "Kappa overall = 0.201715644569542"
## [1] "Tau overall = 0.216524472384374"
## [1] "mean quality = 0.11783115442775"
## [1] "The quality is 0.11783115442775"## Warning in chisq.test(CM): Chi-squared approximation may be incorrect## [1] "######### Cramer's V = NaN"